...Similarity based Analysis of Networks of Ultra Low Resolution Sensors Relevance: Pervasive computing, temporal analysis to discover behaviour Method: MDS, Co-occurrence, HMMs, Agglomerative Clustering, Similarity Analysis Organization: MERL Published: July 2006, Pattern Recognition 39(10) Special Issue on Similarity Based Pattern Recognition Summary: Unsupervised discovery of structure from activations of very low resolution ambient sensors. Methods for discovering location geometry from movement patterns and behavior in an elevator scheduling scenario The context of this work is ambient sensing with a large number of simple sensors (1 bit per second giving on-off info). Two tasks are addressed. Discovering location geometry from patterns of sensor activations. And clustering activation sequences. For the former, a similarity metric is devised that measures the expected time of activation of one sensor after another has been activated, on the assumption that the two activations are resulting from movement. The time is used as a measure of distance between the sensors, and MDS is used to arrive at a geometric distribution. In the second part, the observation sequences are clustered by training HMMs for each sequence, and using agglomerative clustering. Having selected an appropriate number of clusters (chosen by the domain expert) the clusters can be used to train new HMM models. The straightforward mapping of the cluster HMMs is to a composite HMM, where each branch of...
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...Problem 1: Data-Based Decision Making Supermarket Product Placement Suppose that we are responsible for managing product placement within a local supermarket. Our shelving units have 6 shelves each and are numbered from 1 to 6—with 1 being the lowest shelf and proceeding upward until the highest shelf is assigned the number 6. While there are many placement options that we should consider, we decide to look for any correlations between the row a product is placed on and its sales. Since we have our data stored in a data warehouse, it is easily accessible and responds quickly to our data request. Consider each of the following: · What judgments can you make regarding the placement of each type of product being considered? Answer - I think that we are more likely to place those items that are in higher demand by customers and those items that the company wants to generate the greatest profit from on the shelves that have the best sales · What is the consequence of making the wrong choice? Answer - Profit decreases, inventory doesn’t turn over · What types of products do you think each of the product groupings represent? Answer - Most likely to sell/greatest profitability to least likely to sell/lowest profitability · What target markets can you associate with each product group? Answer - ? Problem 2: Market Basket Analysis: Association Analysis Example 1: Our data mining program has performed association analysis and has generated...
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...1 Video Data Mining JungHwan Oh University of Texas at Arlington, USA JeongKyu Lee University of Texas at Arlington, USA Sae Hwang University of Texas at Arlington, USA 8 INTRODUCTION Data mining, which is defined as the process of extracting previously unknown knowledge and detecting interesting patterns from a massive set of data, has been an active research area. As a result, several commercial products and research prototypes are available nowadays. However, most of these studies have focused on corporate data — typically in an alpha-numeric database, and relatively less work has been pursued for the mining of multimedia data (Zaïane, Han, & Zhu, 2000). Digital multimedia differs from previous forms of combined media in that the bits representing texts, images, audios, and videos can be treated as data by computer programs (Simoff, Djeraba, & Zaïane, 2002). One facet of these diverse data in terms of underlying models and formats is that they are synchronized and integrated hence, can be treated as integrated data records. The collection of such integral data records constitutes a multimedia data set. The challenge of extracting meaningful patterns from such data sets has lead to research and development in the area of multimedia data mining. This is a challenging field due to the non-structured nature of multimedia data. Such ubiquitous data is required in many applications such as financial, medical, advertising and Command, Control, Communications and Intelligence...
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...As a group, we chose Data Mining for our project and in specific, an organization very popular and well used, Facebook. Facebook was founded on February 4, 2004 and its mission or purpose was to provide a social media platform. There is not a subscription cost and the platform is freely available to anyone with an internet connection and email address. However, there are advertising tools which one can pay for to ensure your various posts reach more of an audience. For example, you can set up a page specifically to market T-shirts to people and if you want to ensure that many people see it, you can pay Facebook to “push” your post to the maximum amount of people who are likely to be interested in such a product. The services offered are: 1.Communication with friends and family 2.Advertising 3.Marketing 4.Sharing information We chose this organization because it is a very common and popular organization that most of us use and have interest in. Additionally, data mining has become something the general public is aware of because of people like Edward Snowden, formerly of the NSA, and Julian Assange of WikiLeaks. Data mining is the collection of numbers or just information in general. This information is collected for statistical purposes. The data that is collected is processed through mega computers. These computers sift through information and arrange it in a much easier to understand format. Data mining software is one of a number of analytical...
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...Data Mining Nabeel Ahmed University of Northern Virginia Abstract ‘The vein of research data is almost always richer than it appears to be on the surface, but it can only be of value if mined.—Morris Rosenberg’ (AGOSTA, 2000) Recent years, Data Mining has become hot topic of enterprises. More and more companies intend to introduce data mining techniques. One report from the United States treats data mining as one of the ten favorable fields in the 21st century, of which by means shows its importance. Generally speaking, data mining are often applied in those fields, such as insurance and finance industries, retailing and direct marketing industries, communication industry, manufacturing industry and Medical service industry, etc. The data related to management decision making has been accumulating surprisingly quickly because of the improvement in high technology. As the byproduct of internet, e-commerce, e-banking, pos system, barcode scanner and intelligent robot, the acquirement of electronic data has already become cheap and existing everywhere. These data are normally stored in data warehouse and data marts to provide assistance for management decision-making. Data mining is a fast growing field, its main target is to develop some techniques to assist the managers in intelligent analyzing and utilizing mass data. Data mining was already being reported in successfully utilized in the aspects of credit rating, fraud detection, database marketing, customer relationship...
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...This part gives you the concept of multi-level association rule or generalized association rule. 基本阅读：英文资料 5.1,5.2.1 和 5.2.2，这部分内容与老师上课所介 绍的内容一致，不必过分专注于其中的算法和代码部分，更重要的是 理解方法意思,过程及其中的相关例子。扩展阅读：为了解决作业问 题 2 中的（c）小问，你还最好阅读 5.3.1 部分。 Mining Frequent Patterns, Associations, and Correlations Frequent patterns are patterns (such as itemsets, subsequences, or substructures) that appear in a data set frequently. For example, a set of items, such as milk and bread, that appear frequently together in a transaction data set is a frequent itemset. A subsequence, such as buying first a PC, then a digital camera, and then a memory card, if it occurs frequently in a shopping history database, is a (frequent) sequential pattern. A substructure can refer to different structural forms, such as subgraphs, subtrees, or sublattices, which may be combined with itemsets or subsequences. If a substructure occurs frequently, it is called a (frequent) structured pattern. Finding such frequent patterns plays an essential role in mining associations, correlations, and many other interesting relationships among data. Moreover, it helps in data classification, clustering, and other data mining tasks as well. Thus, frequent pattern mining has become an important data mining task and a focused...
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...Data Mining Prepared by: Kirsten Sullivan Strayer University CIS 500 Dr. Baab September 9, 2012 Data mining is a concept that companies use to gain new customers or clients in an effort to make their business and profits grow. The ability to use data mining can result in the accrual of new customers by taking the new information and advertising to customers who are either not currently utilizing the business's product or also in winning additional customers that may be purchasing from the competitor. Generally, data are any “facts, numbers, or text that can be processed by a computer.”1 Today, organizations are accumulating vast and growing amounts of data in different formats and different databases. This includes operational or transactional data such as, sales, cost, inventory, payroll, and accounting. Data mining also known as “knowledge discovery”, is the process of analyzing data from different perspectives and summarizing it into useful information- information that can then be used to increase revenue, cuts costs, and continue the goals outlined for the company. Data mining consists of five major elements: “Extract, transform, and load transaction data onto the data warehouse system, store and manage the data in a multidimensional database system, provide data access to business analysts and information technology professionals, analyze the data by application software, present the data in a useful format, such as a graph or table.”2...
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...Benefits of data mining to the businesses: Data Mining. Assignment 4 Mustafa Abdullah Strayer University Dr. Jodine Burchell 08/30/2012 Data Mining is a useful tool in the business world today. Data Mining is a process that uses statistical information to gather useful information knowledge from data warehouses. Data Mining can be used for many reasons when gathering information. Businesses that use it are finance, retail and banks for the purpose of finding information on a company or individual. Most business use data mining to predict sales, credit card fraud and to find out what makes the patient ill. HR departments use data mining to predict the value of the employee. Robert (2006)” The eventual goal is to project how much workers will produce over their careers”(para6). This tactic helps companies predict employees who will stay longer in the company as time goes by. The information is then stored into their database to help in the hiring process. “ Robert(2006)”Companies will be able to carry out cost-benefit studies on recruiting, training, and employee retention (along with its counterpart, layoffs)”.Base on this information companies are tired of playing the guessing game but data mining gives them a more accurate look. All the data gathered such as videos email, social media helps the HR understand the person and gives the business clues. Data Mining gives HR the ability to understand a person and search for the best job candidates through social media...
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...computing, middleware, and industry standards as relating to the enterprise data repository. Data warehousing, data mining, and data marts are covered from an enterprise perspective. Policies Faculty and students will be held responsible for understanding and adhering to all policies contained within the following two documents: • • University policies: You must be logged into the student website to view this document. Instructor policies: This document is posted in the Course Materials forum. University policies are subject to change. Be sure to read the policies at the beginning of each class. Policies may be slightly different depending on the modality in which you attend class. If you have recently changed modalities, read the policies governing your current class modality. Course Materials Coronel, C., Morris, S., & Rob, P. (2011). Database systems: Design, implementation and management (9th ed.). Mason, OH: Cengage Learning. Eckerson, W. W. (2011). Performance dashboards: Measuring, monitoring, and managing your business (2nd ed.). Hoboken, NJ: John Wiley & Sons, Inc. Hoffer, J. A., Ramesh, V., & Topi, H. (2011). Modern database management (10th ed.). Upper Saddle River, NJ: Pearson. Linoff, G. S., & Berry, M. J. A. (2011). Data mining techniques: For marketing, sales, and customer relationship management (3rd ed.). Indianapolis, IN: Wiley Publishing, Inc. Ponniah, P. (2010). Data warehousing: Fundamentals for IT professionals (2nd ed.). Hoboken, NJ:...
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...Top Data Management Terms to Know Fifteen essential definitions you need to know Fifteen Essential Data Management Terms We know it’s not always easy to keep up-to-date Contents with the latest data management terms. That’s why we have put together the top fifteen terms and definitions that you and your peers need to know. OLAP (online analytical processing) Star schema What is OLAP (online analytical processing) Fact table OLAP (online analytical processing) is computer processing that enables a Big data analytics Data modeling Ad hoc analysis user to easily and selectively extract and view data from different points of view. For example, a user can request that data be analyzed to display a spreadsheet showing all of a company's beach ball products sold in Florida in the month of July, compare revenue figures with those for the same products in September, and then see a comparison of other product sales in Data visualization Extract, transform, load (ETL) Florida in the same time period. To facilitate this kind of analysis, OLAP data is stored in a multidimensional database. Whereas a relational database can be thought of as two-dimensional, a multidimensional database considers each data attribute (such as product, geographic sales region, and time Association rules (in data mining) Relational database period) as a separate "dimension." OLAP software can locate the intersection of dimensions (all products sold in the...
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...(Online): 2347 - 4718 DATA MINING TECHNIQUES TO ANALYZE CRIME DATA R. G. Uthra, M. Tech (CS) Bharathidasan University, Trichy, India. Abstract: In data mining, Crime management is an interesting application where it plays an important role in handling of crime data. Crime investigation has very significant role of police system in any country. There had been an enormous increase in the crime in recent years. With rapid popularity of the internet, crime information maintained in web is becoming increasingly rampant. In this paper the data mining techniques are used to analyze the web data. This paper presents detailed study on classification and clustering. Classification is the process of classifying the crime type Clustering is the process of combining data object into groups. The construct of scenario is to extract the attributes and relations in the web page and reconstruct the scenario for crime mining. Key words: Crime data analysis, classification, clustering. I. INTRODUCTION Crime is one of the dangerous factors for any country. Crime analysis is the activity in which analysis is done on crime activities. Today criminals have maximum use of all modern technologies and hi-tech methods in committing crimes. The law enforcers have to effectively meet out challenges of crime control and maintenance of public order. One challenge to law enforcement and intelligence agencies is the difficulty of analyzing large volumes of data involved in criminal and...
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...Data Mining: What is Data Mining? Overview Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Continuous Innovation Although data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost. Example For example, one Midwest grocery chain used the data mining capacity of Oracle software to analyze local buying patterns. They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have...
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...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...
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...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...
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...The Situation of Big Data Technology Yu Liu International American University BUS 530: Management Information Systems Matthew Keogh 2015 Summer 2 - Section C Introduction In this paper, I will list the main technologies related to big data. According to the life cycle of the data processing, big data technology can be divided into data collection and pre-processing, data storage and management, data analysis and data mining, data visualization and data privacy and security, and so on. The reason I select topic about big data My major is computer science and I have taken a few courses about data mining before. Nowadays more and more job positions about big data are showing at job seeking website, such as Monster.com. I am planning to learn some mainstream big data technologies like Hadoop. Therefore, I choose big data as my midterm paper topic. Big data in Google Google's big data analytics intelligence applications include customer sentiment analysis, risk analysis, product recommendations, message routing, customer losing prediction, the classification of the legal copy, email content filtering, political tendency forecast, species identification and other aspects. It is said that big data will generate $23 million every day for Google. Some typical applications are as follows: Based on MapReduce, Google's traditional applications include data storage, data analysis, log analysis, search quality and other data analytical applications. Based on Dremel system...
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