Data Mining Introduction Lecture Notes For Chapter 1 Intro

Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining by Tan, Steinbach, Kumar Lots of data is being collected and warehoused Web data, e-commerce purchases at department/ grocery stores Bank/Credit Card transactions Computers have become cheaper and more powerful Competitive Pressure is Strong Provide better, customized services for an edge (e.g. in Customer Relationship Management) Why Mine Data? Commercial Viewpoint Why Mine Data? Scientific Viewpoint Data collected and stored at enormous speeds (GB/hour) remote sensors on a satellite telescopes scanning the skies microarrays generating gene expression data scientific simulations generating terabytes of data traditional techniques infeasible for raw data Data mining may help scientists in classifying and segmenting data in Hypothesis Formation Mining Large Data Sets - Motivation There is often information “hidden” in the data that is not readily evident Human analysts may take weeks to discover useful information Much of the data is never analyzed at all The Data Gap Total new disk (TB) since 1995 Number of analysts From: R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering Applications” disks Units Capacity PBs ,.... disks chart data gap chart data gap data gap What is Data Mining? Many Definitions Non-trivial extraction of implicit, previously unknown and potentially useful information from data Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns What is (not) Data Mining? What is Data Mining? Certain names are more prevalent in certain US locations (O’Brien, O’Rurke, O’Reilly… in Boston area) Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com,) What is not Data Mining? Look up phone number in phone directory Query a Web search engine for information about “Amazon” Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems Traditional Techniques may be unsuitable due to Enormity of data High dimensionality of data Heterogeneous, distributed nature of data Origins of Data Mining Machine Learning/ Pattern Recognition Statistics/ AI Data Mining Database systems Data Mining Tasks Prediction Methods Use some variables to predict unknown or future values of other variables. Description Methods Find human-interpretable patterns that describe the data. From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996 Data Mining Tasks... Classification [Predictive] Clustering [Descriptive] Association Rule Discovery [Descriptive] Sequential Pattern Discovery [Descriptive] Regression [Predictive] Deviation Detection [Predictive] Classification: Definition Given a collection of records (training set) Each record contains a set of attributes, one of the attributes is the class. Find a model for class attribute as a function of the values of other attributes. Goal: previously unseen records should be assigned a class as accurately as possible. A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. Classification Example categorical categorical continuous class Training Set Learn Classifier Test Set Model Tid Refund Marital Status Taxable Income Cheat 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes 10 Refund Marital Status Taxable Income Cheat No Single 75K ? Yes Married 50K ? No Married 150K ? Yes Divorced 90K ? No Single 40K ? No Married 80K ? 10 Classification: Application 1 Direct Marketing Goal: Reduce cost of mailing by targeting a set of consumers likely to buy a new cell-phone product. Approach: Use the data for a similar product introduced before. We know which customers decided to buy and which decided otherwise. This {buy, don’t buy} decision forms the class attribute. Collect various demographic, lifestyle, and company-interaction related information about all such customers. Type of business, where they stay, how much they earn, etc. Use this information as input attributes to learn a classifier model. From [Berry & Linoff] Data Mining Techniques, 1997 Classification: Application 2 Fraud Detection Goal: Predict fraudulent cases in credit card transactions. Approach: Use credit card transactions and the information on its account-holder as attributes. When does a customer buy, what does he buy, how often he pays on time, etc Label past transactions as fraud or fair transactions. This forms the class attribute. Learn a model for the class of the transactions. Use this model to detect fraud by observing credit card transactions on an account. Classification: Application 3 Customer Attrition/Churn: Goal: To predict whether a customer is likely to be lost to a competitor. Approach: Use detailed record of transactions with each of the past and present customers, to find attributes. How often the customer calls, where he calls, what time-of-the day he calls most, his financial status, marital status, etc. Label the customers as loyal or disloyal. Find a model for loyalty. From [Berry & Linoff] Data Mining Techniques, 1997 Classification: Application 4 Sky Survey Cataloging Goal: To predict class (star or galaxy) of sky objects, especially visually faint ones, based on the telescopic survey images (from Palomar Observatory). 3000 images with 23,040 x 23,040 pixels per image. Approach: Segment the image. Measure image attributes (features) - 40 of them per object. Model the class based on these features. Success Story: Could find 16 new high red-shift quasars, some of the farthest objects that are difficult to find! From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996 Classifying Galaxies Early Intermediate Late Data Size: 72 million stars, 20 million galaxies Object Catalog: 9 GB Image Database: 150 GB Class: Stages of Formation Attributes: Image features, Characteristics of light waves received, etc. Courtesy: Clustering Definition Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that Data points in one cluster are more similar to one another. Data points in separate clusters are less similar to one another. Similarity Measures: Euclidean Distance if attributes are continuous. Other Problem-specific Measures. Illustrating Clustering Euclidean Distance Based Clustering in 3-D space. Intracluster distances are minimized Intercluster distances are maximized Clustering: Application 1 Market Segmentation: Goal: subdivide a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. Approach: Collect different attributes of customers based on their geographical and lifestyle related information. Find clusters of similar customers. Measure the clustering quality by observing buying patterns of customers in same cluster vs. those from different clusters. Clustering: Application 2 Document Clustering: Goal: To find groups of documents that are similar to each other based on the important terms appearing in them. Approach: To identify frequently occurring terms in each document. Form a similarity measure based on the frequencies of different terms. Use it to cluster. Gain: Information Retrieval can utilize the clusters to relate a new document or search term to clustered documents. Illustrating Document Clustering Clustering Points: 3204 Articles of Los Angeles Times. Similarity Measure: How many words are common in these documents (after some word filtering). Category Total Articles Correctly Placed Financial Foreign National Metro Sports Entertainment Clustering of S&P 500 Stock Data Observe Stock Movements every day. Clustering points: Stock-{UP/DOWN} Similarity Measure: Two points are more similar if the events described by them frequently happen together on the same day. We used association rules to quantify a similarity measure. Discovered Clusters Industry Group 1 Applied-Matl-DOWN, Bay-Network-Down, 3-COM-DOWN, Cabletron-Sys-DOWN, CISCO-DOWN, HP-DOWN, DSCI-Comm-DOWN, INTEL-DOWN, LSI-Logic-DOWN, Micron-Tech-DOWN, Texas-Inst-Down, Tellabs-Inc-Down, Natl-Semiconduct-DOWN, Oracle-DOWN, SGI-DOWN, Sun-DOWN Technology1-DOWN 2 Apple-Comp-DOWN, Autodesk-DOWN, DEC-DOWN, ADV-Micro-Device-DOWN, Andrew-Corp-DOWN, Computer-Assoc-DOWN, Circuit-City-DOWN, Compaq-DOWN, EMC-Corp-DOWN, Gen-Inst-DOWN, Motorola-DOWN, Microsoft-DOWN, Scientific-Atl-DOWN Technology2-DOWN 3 Fannie-Mae-DOWN, Fed-Home-Loan-DOWN, MBNA-Corp-DOWN, Morgan-Stanley-DOWN Financial-DOWN 4 Baker-Hughes-UP, Dresser-Inds-UP, Halliburton-HLD-UP, Louisiana-Land-UP, Phillips-Petro-UP, Unocal-UP, Schlumberger-UP Oil-UP Association Rule Discovery: Definition Given a set of records each of which contain some number of items from a given collection; Produce dependency rules which will predict occurrence of an item based on occurrences of other items. Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer} TID Items 1 Bread, Coke, Milk 2 Beer, Bread 3 Beer, Coke, Diaper, Milk 4 Beer, Bread, Diaper, Milk 5 Coke, Diaper, Milk Association Rule Discovery: Application 1 Marketing and Sales Promotion: Let the rule discovered be {Bagels, … } --> {Potato Chips} Potato Chips as consequent => Can be used to determine what should be done to boost its sales. Bagels in the antecedent => Can be used to see which products would be affected if the store discontinues selling bagels. Bagels in antecedent and Potato chips in consequent => Can be used to see what products should be sold with Bagels to promote sale of Potato chips! Association Rule Discovery: Application 2 Supermarket shelf management. Goal: To identify items that are bought together by sufficiently many customers. Approach: Process the point-of-sale data collected with barcode scanners to find dependencies among items. A classic rule -- If a customer buys diaper and milk, then he is very likely to buy beer. So, don’t be surprised if you find six-packs stacked next to diapers! Association Rule Discovery: Application 3 Inventory Management: Goal: A consumer appliance repair company wants to anticipate the nature of repairs on its consumer products and keep the service vehicles equipped with right parts to reduce on number of visits to consumer households. Approach: Process the data on tools and parts required in previous repairs at different consumer locations and discover the co-occurrence patterns. Sequential Pattern Discovery: Definition Given is a set of objects, with each object associated with its own timeline of events, find rules that predict strong sequential dependencies among different events. Rules are formed by first discovering patterns. Event occurrences in the patterns are governed by timing constraints. (A B) (C) (D E) ng (Fire_Alarm) In point-of-sale transaction sequences, Computer Bookstore: (Intro_To_Visual_C) (C++_Primer) --> (Perl_for_dummies,Tcl_Tk) Athletic Apparel Store: (Shoes) (Racket, Racketball) --> (Sports_Jacket) Regression Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency. Greatly studied in statistics, neural network fields. Examples: Predicting sales amounts of new product based on advertising expenditure. Predicting wind velocities as a function of temperature, humidity, air pressure, etc. Time series prediction of stock market indices. Deviation/Anomaly Detection Detect significant deviations from normal behavior Applications: Credit Card Fraud Detection Network Intrusion Detection Typical network traffic at University level may reach over 100 million connections per day Challenges of Data Mining Scalability Dimensionality Complex and Heterogeneous Data Data Quality Data Ownership and Distribution Privacy Preservation Streaming Data

Paper For Above instruction

Introduction to Data Mining and Its Significance

Data mining refers to the process of discovering meaningful patterns, trends, and relationships within large datasets that are often complex, high-dimensional, and heterogeneous. It involves extracting implicit, previously unknown, and potentially useful information through exploration and analysis, employing various techniques derived from machine learning, statistics, pattern recognition, and database systems (Fayyad et al., 1996). The proliferation of digital data—generated at astonishing speeds by web activities, scientific instruments, sensors, and transactions—necessitates sophisticated methods like data mining to interpret and utilize this information effectively. Traditional techniques become infeasible in managing the vast volume of raw data, emphasizing the importance of data mining for scientific discovery, business insights, and strategic decision-making (Tan, Steinbach, & Kumar, 2006).

Objectives and Applications of Data Mining

Data mining encompasses a range of tasks aimed at understanding and predicting behaviors or characteristics within data. These include classification, clustering, association rule discovery, sequential pattern discovery, regression, and deviation detection (Fayyad et al., 1996). For example, in classification, models are built to predict category labels—such as whether a transaction is fraudulent or if a customer will churn—using historical data. Clustering groups similar data points to identify customer segments, documents, or spatial objects. Association rule discovery finds dependencies, like products frequently bought together, which is widely used in market basket analysis. Sequential pattern discovery investigates ordered events, useful in telecommunication logs or transaction sequences. Regression predicts continuous outcomes like sales or weather variables, while deviation detection identifies anomalies, such as credit card fraud or network intrusions (Berry & Linoff, 1997; Han, Kamber, & Pei, 2011).

Data Mining Techniques and Tasks

Effective data mining relies on various techniques tailored to specific tasks. Classification involves developing models to assign class labels to previously unseen data. For example, credit card transactions can be classified as fraudulent or legitimate based on features such as transaction amount, location, and timing. Clustering partitions data into meaningful groups based on similarity measures like Euclidean distance or other domain-specific metrics, facilitating applications like market segmentation or document grouping (Jain, 2010). Association rule mining uncovers dependencies between items, such as products bought together, critical for marketing, inventory management, and shelf arrangement. Sequential pattern discovery identifies temporal dependencies among events, aiding in predictive maintenance and behavioral analysis. Regression models, often linear or nonlinear, forecast continuous variables like sales figures or environmental parameters. Anomaly detection is crucial in security contexts, pinpointing deviations from normal patterns to detect fraud or cyber threats (Agrawal, Imieliński, & Swami, 1993; Han, Kamber, & Pei, 2011).

Classification in Data Mining and Its Diverse Applications

Classification is a supervised learning task where the goal is to predict the category of a given record based on its attributes. This involves training a model on labeled data and then applying this model to assign classes to new, unseen data. For example, banks employ classification algorithms to detect fraudulent transactions by analyzing features such as transaction amount, time, and location (Fayyad et al., 1996). Similarly, telecommunication companies classify customers based on usage patterns to predict churn, enabling targeted retention strategies. In healthcare, classification models assist in disease diagnosis by interpreting patient data. The effectiveness of classification hinges on quality data, appropriate feature selection, and rigorous validation, often via metrics like accuracy, precision, and recall (Kohavi & John, 1997; Breiman, 2001).

Clustering and Its Utilization in Segmenting Data

Clustering is an unsupervised learning technique that groups data points into clusters such that points within a cluster are more similar to each other than to those outside the cluster. Similarity measures like Euclidean distance facilitate this process in continuous data spaces (Jain, 2010). Applications of clustering include market segmentation, where customers are grouped based on demographic and behavioral features for targeted marketing (Wedel & Kamakura, 2000). Document clustering enables organization of large text corpora into meaningful groups for improved information retrieval. Clustering also applies in bioinformatics for grouping genetic data or in financial markets to identify stocks with similar movement patterns. Successful clustering improves understanding of underlying data structure, enhances decision-making, and supports personalization strategies (Han, Kamber, & Pei, 2011).

Association Rule Mining and Its Practical Use Cases

Association rule mining discovers interesting dependencies among items in transactional datasets. The classic example involves market basket analysis, where rules like "if a customer buys diapers and milk, they are likely to buy beer" help retailers optimize product placement and promotional strategies (Agrawal, Imieliński, & Swami, 1993). Other applications include shelf management, inventory control, and cross-selling. These rules are generated by analyzing co-occurrence patterns and support/confidence metrics, enabling businesses to increase sales and improve supply chain efficiency. For example, in supermarket shelving, understanding which items are commonly purchased together allows for strategic product placement. Similarly, in service industries, co-occurrence of service requests informs maintenance scheduling and parts stocking (Brin et al., 1997).

Sequential Pattern Discovery in Temporal Data Analysis

Sequential pattern discovery focuses on identifying ordered sequences where events are linked over time, with relevance to behaviors and temporal dependencies. This technique is employed in telecommunications to predict failures, such as the progression from alarm logs to fire incidents. In retail, it helps understand shopping sequences, like the typical progression from viewing a product to purchasing related items. Its applications extend to bioinformatics for tracking gene expression over time and in security for detecting attack patterns (Agrawal & Srikant, 1990). Timing constraints govern the patterns’ occurrence; thus, algorithms consider time windows to extract meaningful sequential rules. These insights facilitate proactive responses and strategic planning in various domains (Raghavan, Katz, & Krishnan, 2004).

Regression Analysis for Continuous Variable Prediction

Regression models predict continuous outcomes based on one or more predictor variables, assuming a linear or nonlinear relationship. Linear regression, a fundamental technique, models the dependent variable as a linear combination of inputs. It is widely used in sales forecasting, weather prediction, and economics (Montgomery, Peck, & Vining, 2012). For instance, regression models can predict sales based on advertising spend, or wind velocity as a function of meteorological variables. Advanced regression methods,