Free Essay

Data Mining

In:

Submitted By Harishbolt
Words 3011
Pages 13
Gel electrophoresis
Gel electrophoresis is a method for separation and analysis of macromolecules (DNA, RNA and proteins) and their fragments, based on their size and charge.
It is used in clinical chemistry to separate proteins by charge and/or size (IEF agarose, essentially size independent) and in biochemistry and molecular biology to separate a mixed population of DNA and RNA fragments by length, to estimate the size of DNA and RNA fragments or to separate proteins by charge.[1] Nucleic acid molecules are separated by applying an electric field to move the negatively charged molecules through an agarose matrix. Shorter molecules move faster and migrate farther than longer ones because shorter molecules migrate more easily through the pores of the gel. This phenomenon is called sieving.[2] Proteins are separated by charge in agarose because the pores of the gel are too large to sieve proteins. Gel electrophoresis can also be used for separation of nanoparticles.
Gel electrophoresis uses a gel as an anticonvective medium and/or sieving medium during electrophoresis, the movement of a charged particle in an electrical field. Gels suppress the thermal convection caused by application of the electric field, and can also act as a sieving medium, retarding the passage of molecules; gels can also simply serve to maintain the finished separation, so that a post electrophoresis stain can be applied.[3] DNA Gel electrophoresis is usually performed for analytical purposes, often after amplification of DNA via PCR, but may be used as a preparative technique prior to use of other methods such as mass spectrometry, RFLP, PCR, cloning, DNA sequencing, or Southern blotting for further characterization.
Physical basis
In simple terms: Electrophoresis is a process which enables the sorting of molecules based on size. Using an electric field, molecules (such as DNA) can be made to move through a gel made of agar or polyacrylamide. The electric field consists of a negative charge at one end which pushes the molecules through the gel, and a positive charge at the other end that pulls the molecules through the gel. The molecules being sorted are dispensed into a well in the gel material. The gel is placed in an electrophoresis chamber, which is then connected to a power source. When the electric current is applied, the larger molecules move more slowly through the gel while the smaller molecules move faster. The different sized molecules form distinct bands on the gel.[citation needed] These bands are often used in crime scenes to identify suspects.
The term "gel" in this instance refers to the matrix used to contain, then separate the target molecules. In most cases, the gel is a crosslinked polymer whose composition and porosity is chosen based on the specific weight and composition of the target to be analyzed. When separating proteins or small nucleic acids (DNA, RNA, or oligonucleotides) the gel is usually composed of different concentrations of acrylamide and a cross-linker, producing different sized mesh networks of polyacrylamide. When separating larger nucleic acids (greater than a few hundred bases), the preferred matrix is purified agarose. In both cases, the gel forms a solid, yet porous matrix. Acrylamide, in contrast to polyacrylamide, is a neurotoxin and must be handled using appropriate safety precautions to avoid poisoning. Agarose is composed of long unbranched chains of uncharged carbohydrate without cross links resulting in a gel with large pores allowing for the separation of macromolecules and macromolecular complexes.[citation needed]
Types of gel
The types of gel most typically used are agarose and polyacrylamide gels. Each type of gel is well-suited to different types and sizes of analyte. Polyacrylamide gels are usually used for proteins, and have very high resolving power for small fragments of DNA (5-500 bp). Agarose gels on the other hand have lower resolving power for DNA but have greater range of separation, and are therefore used for DNA fragments of usually 50-20,000 bp in size, but resolution of over 6 Mb is possible with pulsed field gel electrophoresis (PFGE).[5] Polyacrylamide gels are run in a vertical configuration while agarose gels are typically run horizontally in a submarine mode. They also differ in their casting methodology, as agarose sets thermally, while polyacrylamide forms in a chemical polymerization reaction.
Agarose
Agarose gels are easily cast and handled compared to other matrices, because the gel setting is a physical rather than chemical change. Samples are also easily recovered. After the experiment is finished, the resulting gel can be stored in a plastic bag in a refrigerator.
Agarose gels do not have a uniform pore size, but are optimal for electrophoresis of proteins that are larger than 200 kDa.[6] Agarose gel electrophoresis can also be used for the separation of DNA fragments ranging from 50 base pair to several megabases (millions of bases), the largest of which require specialized apparatus. The distance between DNA bands of different lengths is influenced by the percent agarose in the gel, with higher percentages requiring longer run times, sometimes days. Instead high percentage agarose gels should be run with a pulsed field electrophoresis (PFE), or field inversion electrophoresis.
"Most agarose gels are made with between 0.7% (good separation or resolution of large 5–10kb DNA fragments) and 2% (good resolution for small 0.2–1kb fragments) agarose dissolved in electrophoresis buffer. Up to 3% can be used for separating very tiny fragments but a vertical polyacrylamide gel is more appropriate in this case. Low percentage gels are very weak and may break when you try to lift them. High percentage gels are often brittle and do not set evenly. 1% gels are common for many applications."[7]
Polyacrylamide
Main article: Polyacrylamide gel electrophoresis
Polyacrylamide gel electrophoresis (PAGE) is used for separating proteins ranging in size from 5 to 2,000 kDa due to the uniform pore size provided by the polyacrylamide gel. Pore size is controlled by controlling the concentrations of acrylamide and bis-acrylamide powder used in creating a gel. Care must be used when creating this type of gel, as acrylamide is a potent neurotoxin in its liquid and powdered form.
Traditional DNA sequencing techniques such as Maxam-Gilbert or Sanger methods used polyacrylamide gels to separate DNA fragments differing by a single base-pair in length so the sequence could be read. Most modern DNA separation methods now use agarose gels, except for particularly small DNA fragments. It is currently most often used in the field of immunology and protein analysis, often used to separate different proteins or isoforms of the same protein into separate bands. These can be transferred onto a nitrocellulose or PVDF membrane to be probed with antibodies and corresponding markers, such as in a western blot.
Typically resolving gels are made in 6%, 8%, 10%, 12% or 15%. Stacking gel (5%) is poured on top of the resolving gel and a gel comb (which forms the wells and defines the lanes where proteins, sample buffer and ladders will be placed) is inserted. The percentage chosen depends on the size of the protein that one wishes to identify or probe in the sample. The smaller the known weight, the higher the percentage that should be used. Changes on the buffer system of the gel can help to further resolve proteins of very small sizes.[8]
Starch
Partially hydrolysed potato starch makes for another non-toxic medium for protein electrophoresis. The gels are slightly more opaque than acrylamide or agarose. Non-denatured proteins can be separated according to charge and size. They are visualised using Napthal Black or Amido Black staining. Typical starch gel concentrations are 5% to 10%.[9][10][11]
Gel conditions
Denaturing

TTGE profiles representing the bifidobacterial diversity of fecal samples from two healthy volunteers (A and B) before and after AMC (Oral Amoxicillin-Clavulanic Acid) treatment
Denaturing gels are run under conditions that disrupt the natural structure of the analyte, causing it to unfold into a linear chain. Thus, the mobility of each macromolecule depends only on its lineal length and its mass-to-charge ratio. Thus, the secondary, tertiary, and quaternary levels of biomolecular structure are disrupted, leaving only the primary structure to be analyzed.
Nucleic acids are often denatured by including urea in the buffer, while proteins are denatured using sodium dodecyl sulfate, usually as part of the SDS-PAGE process. For full denaturation of proteins, it is also necessary to reduce the covalent disulfide bonds that stabilize their tertiary and quaternary structure, a method called reducing PAGE. Reducing conditions are usually maintained by the addition of beta-mercaptoethanol or dithiothreitol. For general analysis of protein samples, reducing PAGE is the most common form of protein electrophoresis.
Denaturing conditions are necessary for proper estimation of molecular weight of RNA. RNA is able to form more intramolecular interactions than DNA which may result in change of its electrophoretic mobility. Urea, DMSO and glyoxal are the most often used denaturing agents to disrupt RNA structure. Originally, highly toxic methylmercury hydroxide was often used in denaturing RNA electrophoresis,[12] but it may be method of choice for some samples.[13]
Denaturing gel electrophoresis is used in the DNA and RNA banding pattern-based methods DGGE (denaturing gradient gel electrophoresis),[14] TGGE (temperature gradient gel electrophoresis), and TTGE (temporal temperature gradient electrophoresis).[15]
Native

Specific enzyme-linked staining: Glucose-6-Phosphate Dehydrogenase isoenzymes in Plasmodium falciparum infected Red blood cells[16]
Native gels are run in non-denaturing conditions, so that the analyte's natural structure is maintained. This allows the physical size of the folded or assembled complex to affect the mobility, allowing for analysis of all four levels of the biomolecular structure. For biological samples, detergents are used only to the extent that they are necessary to lyse lipid membranes in the cell. Complexes remain—for the most part—associated and folded as they would be in the cell. One downside, however, is that complexes may not separate cleanly or predictably, as it is difficult to predict how the molecule's shape and size will affect its mobility.
Unlike denaturing methods, native gel electrophoresis does not use a charged denaturing agent. The molecules being separated (usually proteins or nucleic acids) therefore differ not only in molecular mass and intrinsic charge, but also the cross-sectional area, and thus experience different electrophoretic forces dependent on the shape of the overall structure. For proteins, since they remain in the native state they may be visualised not only by general protein staining reagents but also by specific enzyme-linked staining.
Native gel electrophoresis is typically used in proteomics and metallomics.[17] However, native SDS-PAGE is also used to scan genes (DNA) for unknown mutations as in Single-strand conformation polymorphism.
Buffers
Buffers in gel electrophoresis are used to provide ions that carry a current and to maintain the pH at a relatively constant value. There are a number of buffers used for electrophoresis. The most common being, for nucleic acids Tris/Acetate/EDTA (TAE), Tris/Borate/EDTA (TBE). Many other buffers have been proposed, e.g. lithium borate, which is almost never used, based on Pubmed citations (LB), iso electric histidine, pK matched goods buffers, etc.; in most cases the purported rationale is lower current (less heat) and or matched ion mobilities, which leads to longer buffer life. Borate is problematic; Borate can polymerize, and/or interact with cis diols such as those found in RNA. TAE has the lowest buffering capacity but provides the best resolution for larger DNA. This means a lower voltage and more time, but a better product. LB is relatively new and is ineffective in resolving fragments larger than 5 kbp; However, with its low conductivity, a much higher voltage could be used (up to 35 V/cm), which means a shorter analysis time for routine electrophoresis. As low as one base pair size difference could be resolved in 3% agarose gel with an extremely low conductivity medium (1 mM Lithium borate).[18]
Most SDS-PAGE protein separations are performed using a "discontinuous" (or DISC) buffer system that significantly enhances the sharpness of the bands within the gel. During electrophoresis in a discontinuous gel system, an ion gradient is formed in the early stage of electrophoresis that causes all of the proteins to focus into a single sharp band in a process called isotachophoresis. Separation of the proteins by size is achieved in the lower, "resolving" region of the gel. The resolving gel typically has a much smaller pore size, which leads to a sieving effect that now determines the electrophoretic mobility of the proteins.
Visualization
Further information: Gel electrophoresis of nucleic acids#Visualization and Gel electrophoresis of proteins#Visualization
After the electrophoresis is complete, the molecules in the gel can be stained to make them visible. DNA may be visualized using ethidium bromide which, when intercalated into DNA, fluoresce under ultraviolet light, while protein may be visualised using silver stain or Coomassie Brilliant Blue dye. Other methods may also be used to visualize the separation of the mixture's components on the gel. If the molecules to be separated contain radioactivity, for example in DNA sequencing gel, an autoradiogram can be recorded of the gel. Photographs can be taken of gels, often using Gel Doc.
Downstream processing
After separation, an additional separation method may then be used, such as isoelectric focusing or SDS-PAGE. The gel will then be physically cut, and the protein complexes extracted from each portion separately. Each extract may then be analysed, such as by peptide mass fingerprinting or de novo sequencing after in-gel digestion. This can provide a great deal of information about the identities of the proteins in a complex.
Applications
* Estimation of the size of DNA molecules following restriction enzyme digestion, e.g. in restriction mapping of cloned DNA. * Analysis of PCR products, e.g. in molecular genetic diagnosis or genetic fingerprinting * Separation of restricted genomic DNA prior to Southern transfer, or of RNA prior to Northern transfer.
Gel electrophoresis is used in forensics, molecular biology, genetics, microbiology and biochemistry. The results can be analyzed quantitatively by visualizing the gel with UV light and a gel imaging device. The image is recorded with a computer operated camera, and the intensity of the band or spot of interest is measured and compared against standard or markers loaded on the same gel. The measurement and analysis are mostly done with specialized software.
Depending on the type of analysis being performed, other techniques are often implemented in conjunction with the results of gel electrophoresis, providing a wide range of field-specific applications.
Nucleic acids
Main article: Gel electrophoresis of nucleic acids

An agarose gel of a PCR product compared to a DNA ladder.
In the case of nucleic acids, the direction of migration, from negative to positive electrodes, is due to the naturally occurring negative charge carried by their sugar-phosphate backbone.[19]
Double-stranded DNA fragments naturally behave as long rods, so their migration through the gel is relative to their size or, for cyclic fragments, their radius of gyration. Circular DNA such as plasmids, however, may show multiple bands, the speed of migration may depend on whether it is relaxed or supercoiled. Single-stranded DNA or RNA tend to fold up into molecules with complex shapes and migrate through the gel in a complicated manner based on their tertiary structure. Therefore, agents that disrupt the hydrogen bonds, such as sodium hydroxide or formamide, are used to denature the nucleic acids and cause them to behave as long rods again.[20]
Gel electrophoresis of large DNA or RNA is usually done by agarose gel electrophoresis. See the "Chain termination method" page for an example of a polyacrylamide DNA sequencing gel. Characterization through ligand interaction of nucleic acids or fragments may be performed by mobility shift affinity electrophoresis.
Electrophoresis of RNA samples can be used to check for genomic DNA contamination and also for RNA degradation. RNA from eukaryotic organisms shows distinct bands of 28s and 18s rRNA, the 28s band being approximately twice as intense as the 18s band. Degraded RNA has less sharpely defined bands, has a smeared appearance, and intensity ratio is less than 2:1.
Proteins
Main article: Gel electrophoresis of proteins

SDS-PAGE autoradiography – The indicated proteins are present in different concentrations in the two samples.
Proteins, unlike nucleic acids, can have varying charges and complex shapes, therefore they may not migrate into the polyacrylamide gel at similar rates, or at all, when placing a negative to positive EMF on the sample. Proteins therefore, are usually denatured in the presence of a detergent such as sodium dodecyl sulfate (SDS) that coats the proteins with a negative charge.[3] Generally, the amount of SDS bound is relative to the size of the protein (usually 1.4g SDS per gram of protein), so that the resulting denatured proteins have an overall negative charge, and all the proteins have a similar charge to mass ratio. Since denatured proteins act like long rods instead of having a complex tertiary shape, the rate at which the resulting SDS coated proteins migrate in the gel is relative only to its size and not its charge or shape.[3]
Proteins are usually analyzed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), by native gel electrophoresis, by quantitative preparative native continuous polyacrylamide gel electrophoresis (QPNC-PAGE), or by 2-D electrophoresis.
Characterization through ligand interaction may be performed by electroblotting or by affinity electrophoresis in agarose or by capillary electrophoresis as for estimation of binding constants and determination of structural features like glycan content through lectin binding.
References
 Kryndushkin DS, Alexandrov IM, Ter-Avanesyan MD, Kushnirov VV (2003). "Yeast [PSI+] prion aggregates are formed by small Sup35 polymers fragmented by Hsp104". Journal of Biological Chemistry 278 (49): 49636–43. doi:10.1074/jbc.M307996200. PMID 14507919.

 Jump up ^ Sambrook J, Russel DW (2001). Molecular Cloning: A Laboratory Manual 3rd Ed. Cold Spring Harbor Laboratory Press. Cold Spring Harbor, NY.

 ^ Jump up to: a b c Berg JM, Tymoczko JL Stryer L (2002). Biochemistry (5th ed.). WH Freeman. ISBN 0-7167-4955-6.

Similar Documents

Premium Essay

Data Mining

...Data Mining 0. Abstract With the development of different fields, artificial intelligence, machine learning, statistic, database, pattern recognition and neurocomputing they merge to a newly technology, the data mining. The ultimate goal of data mining is to obtain knowledge from the large database. It helps to discover previously unknown patterns, most of the time it is followed by deeper manual evaluation to explain and correlate the results to establish a new knowledge. It is often practically used by government, bank, insurance company and medical researcher. A general basic idea of data mining would be introduced. In this article, they are divided into four types, predictive modeling, database segmentation, link analysis and deviation detection. A brief introduction will explain the variation among them. For the next part, current privacy, ethical as well as technical issue regarding data mining will be discussed. Besides, the future development trends, especially concept of the developing sport data mining is written. Last but not the least different views on data mining including the good side, the drawback and our views are integrated into the paragraph. 1. Introduction This century, is the age of digital world. We are no longer able to live without the computing technology. Due to information explosion, we are having difficulty to obtain knowledge from large amount of unorganized data. One of the solutions, Knowledge Discovery in Database (KDD) is introduced...

Words: 1700 - Pages: 7

Premium Essay

Data Mining

...Data mining is an iterative process of selecting, exploring and modeling large amounts of data to identify meaningful, logical patterns and relationships among key variables.  Data mining is used to uncover trends, predict future events and assess the merits of various courses of action.             When employing, predictive analytics and data mining can make marketing more efficient. There are many techniques and methods, including business intelligence data collection. Predictive analytics is using business intelligence data for forecasting and modeling. It is a way to use predictive analysis data to predict future patterns. It is used widely in the insurance, medical and credit industries. Assessment of credit, and assignment of a credit score is probably the most widely known use of predictive analytics. Using events of the past, managers are able to estimate the likelihood of future events. Data mining aids predictive analysis by providing a record of the past that can be analyzed and used to predict which customers are most likely to renew, purchase, or purchase related products and services. Business intelligence data mining is important to your marketing campaigns. Proper data mining algorithms and predictive modeling can narrow your target audience and allow you to tailor your ads to each online customer as he or she navigates your site. Your marketing team will have the opportunity to develop multiple advertisements based on the past clicks of your visitors. Predictive...

Words: 1136 - Pages: 5

Premium Essay

Data Mining

...Data Mining Jenna Walker Dr. Emmanuel Nyeanchi Information Systems Decision Making May 30, 2012 Abstract Businesses are utilizing techniques such as data mining to create a competitive advantage customer loyalty. Data mining allows business to analyze customer information, such as demographics and purchase history for a better understanding of what the customers need and what they will respond to. Data mining currently takes place in several industries, and will only become even more widespread as the benefits are endless. The purpose of this paper is to gain research and examine data mining, its benefits to businesses, and issues or concerns it will need to overcome. Real world case studies of how data mining is used will also be presented for a deeper understanding. This study will show that despite its disadvantages, data mining is an important step for a business to better understand its customers, and is the future of business marking and operational planning. Tools and Benefits of data mining Before examining the benefits of data mining, it is important to understand what data mining is exactly. Data mining is defined as “a process that uses statistical, mathematical, artificial intelligence, and machine-learning techniques to extract and identify useful information and subsequent knowledge from large databases, including data warehouses” (Turban & Volonino, 2011). The information identified using data mining includes patterns indicating trends...

Words: 1900 - Pages: 8

Premium Essay

Data Mining

...Data Mining Professor Clifton Howell CIS500-Information Systems Decision Making March 7, 2014 Benefits of data mining to the businesses One of the benefits to data mining is the ability to utilize information that you have stored to predict the possibilities of consumer’s actions and needs to make better business decisions. We implement a business intelligence that will produce a predictive score for those consumers to determine these possibilities. Predictive analytics is the business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model which has, in turn, been trained over your data, learning from the experience of your organization. (Impact, 2014) The usefulness of predictive scoring is obvious. However, with no predictive model and no means to score your consumer, the possibility of gaining a competitive edge and revenue is also predictable. To discover consumer buying patterns from a transaction database, mining association rules are used to make better business decisions. However because users may only be interested in certain information from this database and do not want to invest a lot of time in searching for what they need, association discovery will assist in limiting the data to which only the end user needs. Association discovery will utilize algorithms to lessen the quantity of groupings of item sets or sequences in each customer...

Words: 1318 - Pages: 6

Premium Essay

Data Mining

...Data Mining Teresa M. Tidwell Dr. Sergey Samoilenko Information Systems for Decision Making September 2, 2012 Data Mining The use of data mining by companies assists them with identifying information and knowledge from databases and data warehouses that would be beneficial for the company. The information is often buried in databases, records, and files. With the use of tools such as queries and algorithms, companies can access data, analyze it, and use it to increase their profit. The benefits of using data mining, its reliability, and privacy concerns will be discussed. Benefits of Data Mining 1. Predictive Analytics: This type of analysis uses the customer’s data to make a specific model for the business. Existing information is used such as a customer’s recent purchases and their income, to create a prediction of future purchases and how much or what type of item would be purchased. The more variables used the more likely that the prediction will be correct. Such variables include the customer ranking, based on the number of and most recent purchases and the average profit made per customer purchase. Without data made available through web access and purchases by the customer, predictive analysis would be difficult to perform. The company, therefore, would not be able to plan nor predict how well they are performing. 2. Associations Discovery: This part of data mining helps the company to discover the “relationships hidden in larger data sets” (Pang-Ning...

Words: 1443 - Pages: 6

Premium Essay

Data Mining

...The increasing use of data mining by corporations and advertisers obviously creates apprehension from the perspective of the individual consumer due to privacy, security and the potential use of inaccurate information. The idea that there are data warehouses that contain customers’ personal information can be rather frightening. However, the use of data mining by these organizations can also lead to numerous benefits for consumers they otherwise would not have realized. Besides the obvious benefit of guiding consumers to products or services they’d be more interested in purchasing, the use of data mining by companies has also benefitted individuals’ health and financial safety. Not long after the use of data mining came into prominence the use of data mining consumer information vs. consumer privacy became a major issue in early 1998 after CVS and Giant entered into an agreement with Elensys, a Massachusetts direct marketing company, to send reminders to customers who had not renewed their prescriptions. However, neither CVS nor Giant explained how the program would work or received their customers' permission to transfer their prescription records to a third party (Pyatt Jr.). Giant and CVS’s rationale for entering into this agreement was “to develop a positive program to benefit consumers, many of whom don't take their medication properly,” (Pyatt Jr.). Even though their primary intention was good, Giant and CVS were not transparent about their agreement with Elensys...

Words: 949 - Pages: 4

Premium Essay

Data Mining

...Data Mining Objectives: Highlight the characteristics of Data mining Operations, Techniques and Tools. A Brief Overview Online Analytical Processing (OLAP): OLAP is the dynamic synthesis, analysis, and consolidation of large volumns of multi-dimensional data. Multi-dimensional OLAP support common analyst operations, such as: ▪ Considation – aggregate of data, e.g. roll-ups from branches to regions. ▪ Drill-down – showing details, just the reverse of considation. ▪ Slicing and dicing – pivoting. Looking at the data from different viewpoints. E.g. X, Y, Z axis as salesman, Nth quarter and products, or region, Nth quarter and products. A Brief Overview Data Mining: Construct an advanced architecture for storing information in a multi-dimension data warehouse is just the first step to evolve from traditional DBMS. To realize the value of a data warehouse, it is necessary to extract the knowledge hidden within the warehouse. Unlike OLAP, which reveal patterns that are known in advance, Data Mining uses the machine learning techniques to find hidden relationships within data. So Data Mining is to ▪ Analyse data, ▪ Use software techniques ▪ Finding hidden and unexpected patterns and relationships in sets of data. Examples of Data Mining Applications: ▪ Identifying potential credit card customer groups ▪ Identifying buying patterns of customers. ▪ Predicting trends of market...

Words: 1258 - Pages: 6

Free Essay

Data Mining

...DATA MINING Generally, data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of tools for analyzing data. It allows users to analyze data from many different dimensions or angels, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding patterns among dozens of fields in large databases that are similar to one another. Data is any facts, numbers, or text that can be processed by a computer so in general it makes it easier for a company or business to see what the majority of customers want at a time. It’s almost like a survey that we don’t realize we are taking. I think it really can benefit consumers because we can walk into a place of business and see what we want on the shelves because it is in demand. Even better, the things we don’t want to purchase are not there because there is no demand for it. It gives us the choice to be heard and have a say in making decisions on things that impact us most. Information can be converted into knowledge about historical patterns and future trends. For example, summary information on retail supermarket sales can be analyzed in light of promotional efforts to provide knowledge of consumer buying behavior. Thus, a manufacturer or retailer could determine which items are most susceptible to promotional efforts. I don’t think data...

Words: 1315 - Pages: 6

Premium Essay

Data Mining

...DATA MINING FOR INTELIIGENCE LED POLICING The paper concentrates on use of data mining techniques in police domain using associative memory being the main technique. The author talks of constructing the data being easier and thus giving effective decision making. The author does mention making the process as simple as possible since there are not enough technically sound people into databases. The process involves a step procedural method. Further the author does explain the advantages of this system in police environment. The author mentions use of data mining activities by Dutch forces and how it makes the work easier to predict and analyze the scenario. The author talks about the tool and name given to it as Data detective. This tool involved a chunk of data stored in data warehouse. There has been a continuous development in the tool used here throughout the years making it more efficient than before. The data mining tool automatically predicts the trend and the lays down all the statistical report. This tool makes it easier for the police to pin out criminals and their trends easily. The process raises a challenge so that a predictive modeling can be developed better than before. The author talks about understanding the links and then predicting is important. The author also mentions that this involves pattern matching which is achieved by data mining. The tool also helps in automatic prediction of criminal nature matches a profile and this leads to be quite...

Words: 1306 - Pages: 6

Premium Essay

Data Mining

...Running Head: DATA MINING Assignment 4: Data Mining Submitted by: Submitted to: Course: Introduction Data Mining is also called as Knowledge Discovery in Databases (KDD). It is a powerful technology which has great potential in helping companies to focus on the most important information they have in their data base. Due to the increased use of technologies, interest in data mining has increased speedily. Data mining can be used to predict future behavior rather than focus on past events. This is done by focusing on existing information that may be stored in their data warehouse or information warehouse. Companies are now utilizing data mining techniques to assess their database for trends, relationships, and outcomes to improve their overall operations and discover new ways that may permit them to improve their customer services. Data mining provides multiple benefits to government, businesses, society as well as individual persons (Data Mining, 2011). Benefits of data mining to the businesses when employing Advantages of data mining from business point of view is that large sizes of apparently pointless information have been filtered into important and valuable business information to the company, which could be stored in data warehouses. While in the past, the responsibility was on marketing utilities and services, products, the center of attention is now on customers- their choices, preferences, dislikes and likes, and possibly data mining is one of the most important tools...

Words: 1302 - Pages: 6

Premium Essay

Data Mining

...Data Mining 6/3/12 CIS 500 Data Mining is the process of analyzing data from different perspectives and summarizing it into useful information. This information can be used to increase revenue, cut costs or both. Data mining software is a major analytical tool used for analyzing data. It allows the user to analyze data from many different angles, categorize the data and summarizing the relationships. In a nut shell data mining is used mostly for the process of finding correlations or patterns among fields in very large databases. What ultimately can data mining do for a company? A lot. Data mining is primarily used by companies with strong customer focus in retail or financial. It allows companies to determine relationships among factors such as price, product placement, and staff skill set. There are external factors that data mining can use as well such as location, economic indicators, and competition of other companies. With the use of data mining a retailer can look at point of sale records of a customer purchases to send promotions to certain areas based on purchases made. An example of this is Blockbuster looking at movie rentals to send customers updates regarding new movies depending on their previous rent list. Another example would be American express suggesting products to card holders depending on monthly purchases histories. Data Mining consists of 5 major elements: • Extract, transform, and load transaction data onto the data...

Words: 1012 - Pages: 5

Premium Essay

Data Mining

...Data Mining Introduction to Management Information System 04-73-213 Section 5 Professor Mao March 22, 2011 Group 5: Carol DeBruyn, Jason Rekker, Matt Smith, Mike St. Denis Odette School of Business – The University of Windsor Table of Contents Table of Contents ……………………………………………………………...…….………….. ii Introduction ……………………………………………………………………………………… 1 Data Mining ……………………………………………………………………...……………… 1 Text Mining ……………………………………………………………………...……………… 4 Conclusion ………………………...…………………………………………………………….. 7 References ………………………………………………..……………………………………… 9 Introduction Everyday millions of transactions occur at thousands of businesses. Each transaction provides valuable data to these businesses. This valuable data is then stored in data warehouses and data marts for later reference. This stored data represents a large asset that until the advent of data mining had been largely unexploited. As companies attempt to gain a competitive advantage over each other, new data mining techniques have been developed. The most recent revolution in data mining has resulted in text mining. Prior to text mining, companies could only focus on leveraging their numerical data. Now companies are beginning to benefit from the textual data stored in data warehouses as well. Data Mining Data mining, which is also known as data discovery or knowledge discovery is the procedure that gathers, analyzes and places into perspective useful information. This facilitates the analysis of data from...

Words: 2331 - Pages: 10

Premium Essay

Data Mining

...[pic] Data Mining Assignment 4 [pic] “Data mining software is one of a number of analytical tools for analyzing data (Data Mining, para. 1).” We will be learning about the competitive advantage, reliability of such tool, and privacy concerns towards consumers. Data mining tool is used by majority of companies to increase revenue, and build on the relationship with current consumers. Let’s explore the world of data mining technology in the following selection. “Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data (Data Mining, para. 7).” Data mining is implemented online to promote business ideas, products, and other ways to market them. Data mining is used in political websites, when you go to some sites they take your information then, they began to send you things to promote the Republicans and Democrats message. This is how your voice counts. “Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research...

Words: 1183 - Pages: 5

Premium Essay

Data Mining

...Data Mining By: Holly Gildea CIS 500 Dr. Janet Durgin June 09, 2013 Data Mining We learn that data mining is a method of evaluating data from different viewpoints and summarizing it into useful information. Such information can be beneficial and used to increase things like revenue, and cutting costs, and so on. There are four categories that we will look at and determine the benefits for in regards to data mining: predictive analytics to understand the behavior of customers, associations discovery in products sold to customers, web mining to discover business intelligence from web customers, and clustering to find related customer information. To understand the behavior of customers by the use predictive analytics we must first understand what predictive analytics is. “Predictive analytics is the process of dealing with a variety of data and applying various mathematical formulas to discover the best decision for a given situation” (ArticleSnatch, 2011). This gives any business a competitive edge and helps to remove the guess work out of the decision making process therefore helping to find the right solution in a shorter amount of time. In order to find the solution faster there are a seven simple steps that must be worked thru first: what is the problem for the company, searching for multiple data resources, take the patterns that are observed from that data, creating a model that contains the problem and the data, categorize the data and find important...

Words: 1843 - Pages: 8

Premium Essay

Data Mining

...1. Define data mining. Why are there many different names and definitions for data mining? Data mining is the process through which previously unknown patterns in data were discovered. Another definition would be “a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and subsequent knowledge from large databases.” This includes most types of automated data analysis. A third definition: Data mining is the process of finding mathematical patterns from (usually) large sets of data; these can be rules, affinities, correlations, trends, or prediction models. Data mining has many definitions because it’s been stretched beyond those limits by some software vendors to include most forms of data analysis in order to increase sales using the popularity of data mining. What recent factors have increased the popularity of data mining? Following are some of most pronounced reasons: * More intense competition at the global scale driven by customers’ ever-changing needs and wants in an increasingly saturated marketplace. * General recognition of the untapped value hidden in large data sources. * Consolidation and integration of database records, which enables a single view of customers, vendors, transactions, etc. * Consolidation of databases and other data repositories into a single location in the form of a data warehouse. * The exponential increase...

Words: 4581 - Pages: 19