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Supply Chain Management Data Warehousing Data Mining CPE 665 Enterprise Computing www.cpe.kmutt.ac.th/~suthep/cpe665 Assoc. Prof. Suthep Madarasmi, Ph.D. Enterprise Data Sales & A/R Human Resource Accounts & Finance Asset, Costing Production Mgmt. Ideal MRP Supply Chain/MRP Purchasing & A/P Inventory Control Planning 2 What is SCM? (Supply Chain Mgmt.) Use of IT To Answer: • What are we producing? • What resources are needed? • What do we have, what we need, and when? • How much capacity do we need, and when? • What are pending issues and deliveries for orders? • Why was order Late? • What future production problems will we have? 3 Ideal Information System Top EIS Executive Information System EIS Management Information System MIS Transaction Processing System TPS database 4 MRP (Material Requirements Planning) P1 Issue MO S.O. SO – Sales Order MO – Manufacturing Order WO – Work Order PO – Purchase Order MO / WO Issue PO P.O. In Stock MRP (Material Requirements Planning) Issue MO MO / WO S.O. Issue PO P.O. Receive Raw Mat In Stock MRP (Material Requirements Planning) Issue Raw Mat Issue MO MO / WO S.O. Issue PO P.O. Receive Raw Mat In Stock MRP (Material Requirements Planning) Issue Raw Mat Issue MO MO / WO S.O. Issue PO P.O. Receive Raw Mat Receive Produced Goods In Stock MRP (Material Requirements Planning) Issue Raw Mat Issue MO MO / WO S.O. Issue PO P.O. FG Delivery Receive Raw Mat Receive Produced Goods In Stock MRP: Inventory System Links All Information on goods in/out of warehouse entered by stores department links all MRP information. Orders affect future stock balance. Flows linked to: - Sales Order SO (delivery remain) - Purchase Order PO (receiving remain) - Manufacturing Order MO (raw mat. issues remain, finished goods receiving remain) - Work Order WO (raw mat. issues remain, finished goods receiving remain) MRP Can see Future Stock Balance Can see Future Stock Card Example Orders Data for MRP Example Orders Data for MRP Stores Data with Order References Stock Balance Report Example Stock Card Report Example MRP: Future Stock Balance Data Mining: Concepts and Techniques — Slides for Textbook — — Chapter 2 — © Jiawei Han and Micheline Kamber Multi-Tiered Architecture of data warehousing and data mining other sources Metadata Operational Extract Transform DBs Load Integration Refresh Monitor & Integrator Data Warehouse OLAP Server Serve Analysis Query Reports Data mining Data Marts Data Sources Data Storage OLAP Engine Front-End Tools What Data to put in Warehouse? What Analysis/Query/Reports are Needed? What Data Mining Functions may be done? Example Case Study Tesco Lotus Retail Outlet Data Head: Central Finance System Head & Outlet: Inventory management per warehouse Head: Distribution Plan Head: Supply Chain and Logistics Management Head: Orders Management Outlet: POS (Point of Sales) at Outlet Outlet: Returns Handling Head & Outlet: Human Resource Management Outlet: Customer Satisfaction Survey What Analysis Reports Needed? Sales Analysis Sales by Outlet, Zone, Product Code, Actual Product, Product Category Profit by Supplier, Product, Category Return by Product, Supplier, Sales Zone Payment Methods Used by Customer Trends per Season. Comparison across years. Performance Stores below minimum stock for over ___ days Delays in delivery Sales by Promotion Type, Sales person Goods Lost / Stolen by product, outlet, zone What Data Mining Needed? Product Correlations by Product Code, Actual Product, Product Categories, Outlet, Zone Products with high return chances Credit Card fraud cases Employee Theft cases Member Purchase Patterns Chapter 2: Data Warehousing and OLAP Technology for Data Mining What is a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation Further development of data cube technology From data warehousing to data mining What is Data Warehouse? Defined in many different ways, but not rigorously. A decision support database that is maintained separately from the organization’s operational database Support information processing by providing a solid platform of consolidated, historical data for analysis. “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process.”—W. H. Inmon Data warehousing: The process of constructing and using data warehouses Data Warehouse: Subject-Oriented Organized around major subjects, such as customer, product, sales. Focusing on the modeling and analysis of data for decision makers, not on daily operations or transaction processing. Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process. Data Warehouse:Integrated Constructed by integrating multiple, heterogeneous data sources relational databases, flat files, on-line transaction records Data cleaning and data integration techniques are applied. Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources • E.g., Hotel price: currency, tax, breakfast covered, etc. When data is moved to the warehouse, it is converted. Data Warehouse: Time Variant The time horizon for the data warehouse is significantly longer than that of operational systems. Operational database: current value data. Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years) Every key structure in the data warehouse Contains an element of time, explicitly or implicitly But the key of operational data may or may not contain “time element”. Data Warehouse: Non-Volatile A physically separate store of data transformed from the operational environment. Operational update of data does not occur in the data warehouse environment. Does not require transaction processing, recovery, and concurrency control mechanisms Requires only two operations in data accessing: • initial loading of data and access of data. Data Warehouse vs. Heterogeneous DBMS Traditional heterogeneous DB integration: Build wrappers/mediators on top of heterogeneous databases Query driven approach • When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set • Complex information filtering, compete for resources Data warehouse: update-driven, high performance Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis Data Warehouse vs. Operational DBMS OLTP (on-line transaction processing) Major task of traditional relational DBMS Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc. OLAP (on-line analytical processing) Major task of data warehouse system Data analysis and decision making Distinct features (OLTP vs. OLAP): User and system orientation: customer vs. market Data contents: current, detailed vs. historical, consolidated Database design: ER + application vs. star + subject View: current, local vs. evolutionary, integrated Access patterns: update vs. read-only but complex queries OLTP vs. OLAP OLTP OLAP users clerk, IT professional knowledge worker function day to day operations decision support DB design application-oriented subject-oriented data current, up-to-date detailed, flat relational isolated repetitive historical, summarized, multidimensional integrated, consolidated ad-hoc lots of scans unit of work read/write index/hash on prim. key short, simple transaction # records accessed tens millions #users thousands hundreds DB size 100MB-GB 100GB-TB metric transaction throughput query throughput, response usage access complex query Why Separate Data Warehouse? High performance for both systems DBMS - tuned for OLTP: access methods, indexing, concurrency control, recovery Warehouse - tuned for OLAP: complex OLAP queries, multidimensional view, consolidation. Different functions and different data: missing data: Decision support requires historical data which operational DBs do not typically maintain data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled Data Warehousing and OLAP Technology for Data Mining What is a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation Further development of data cube technology From data warehousing to data mining From Tables and Spreadsheets to Data Cubes A data warehouse is based on a multidimensional data model which views data in the form of a data cube A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions Dimension tables, such as item (item_name, brand, type), or time(day, week, month, quarter, year) Fact table contains measures (such as dollars_sold) and keys to each of the related dimension tables In data warehousing literature, an n-D base cube is called a base cuboid. The top most 0-D cuboid, which holds the highest-level of summarization, is called the apex cuboid. The lattice of cuboids forms a data cube. Cube: A Lattice of Cuboids all time time,item 0-D (apex) cuboid item time,location location item,location time,supplier time,item,location supplier 1-D cuboids location,supplier 2-D cuboids item,supplier time,location,supplier 3-D cuboids time,item,supplier item,location,supplier 4-D (base) cuboid time, item, location, supplier Conceptual Modeling of Data Warehouses Modeling data warehouses: dimensions & measures Star schema: A fact table in the middle connected to a set of dimension tables Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation Example of Star Schema time time_key day day_of_the_week month quarter year item Sales Fact Table time_key item_key item_key item_name brand type supplier_type branch_key location branch location_key branch_key branch_name branch_type units_sold dollars_sold avg_sales Measures location_key street city province_or_street country Example of Snowflake Schema time time_key day day_of_the_week month quarter year item Sales Fact Table time_key item_key branch_key branch location_key branch_key branch_name branch_type units_sold dollars_sold avg_sales Measures item_key item_name brand type supplier_key supplier supplier_key supplier_type location location_key street city_key city city_key city province_or_street country Example of Fact Constellation time time_key day day_of_the_week month quarter year item Sales Fact Table time_key item_key item_key item_name brand type supplier_type location_key branch_key branch_name branch_type units_sold dollars_sold avg_sales Measures time_key item_key shipper_key from_location branch_key branch Shipping Fact Table location to_location location_key street city province_or_street country dollars_cost units_shipped shipper shipper_key shipper_name location_key shipper_type A Data Mining Query Language, DMQL: Language Primitives Cube Definition (Fact Table) define cube <cube_name> [<dimension_list>]: <measure_list> Dimension Definition ( Dimension Table ) define dimension <dimension_name> as (<attribute_or_subdimension_list>) Special Case (Shared Dimension Tables) First time as “cube definition” define dimension <dimension_name> as <dimension_name_first_time> in cube <cube_name_first_time> Defining a Star Schema in DMQL define cube sales_star [time, item, branch, location]: dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars), units_sold = count(*) define dimension time as (time_key, day, day_of_week, month, quarter, year) define dimension item as (item_key, item_name, brand, type, supplier_type) define dimension branch as (branch_key, branch_name, branch_type) define dimension location as (location_key, street, city, province_or_state, country) A Concept Hierarchy: Dimension (location) all all Europe region country city office Germany Frankfurt ... ... ... Spain North_America Canada Vancouver ... L. Chan ... ... Mexico Toronto M. Wind Multidimensional Data Sales volume as a function of product, month, and region Product Dimensions: Product, Location, Time Hierarchical summarization paths Industry Region Year Category Country Quarter Product Month City Office Month Week Day A Sample Data Cube 2Qtr 3Qtr 4Qtr sum U.S.A Canada Mexico sum Country TV PC VCR sum 1Qtr Date Total annual sales of TV in U.S.A. Browsing a Data Cube Visualization OLAP capabilities Interactive manipulation Typical OLAP Operations Roll up (drill-up): summarize data by climbing up hierarchy or by dimension reduction Drill down (roll down): reverse of roll-up from higher level summary to lower level summary or detailed data, or introducing new dimensions Slice and dice: project and select Pivot (rotate): reorient the cube, visualization, 3D to series of 2D planes. Other operations drill across: involving (across) more than one fact table drill through: through the bottom level of the cube to its back-end relational tables (using SQL) Multi-Tiered Architecture other Metadata sources Operational DBs Extract Transform Load Refresh Monitor & Integrator Data Warehouse OLAP Server Serve Analysis Query Reports Data mining Data Marts Data Sources Data Storage OLAP Engine Front-End Tools Three Data Warehouse Models Enterprise warehouse collects all of the information about subjects spanning the entire organization Data Mart a subset of corporate-wide data that is of value to a specific groups of users. Its scope is confined to specific, selected groups, such as marketing data mart • Independent vs. dependent (directly from warehouse) data mart Virtual warehouse A set of views over operational databases Only some of the possible summary views may be materialized Cube Operation Cube definition and computation in DMQL define cube sales[item, city, year]: sum(sales_in_dollars) compute cube sales Transform it into a SQL-like language (with a new operator cube by, introduced by Gray et al.’96) SELECT item, city, year, SUM (amount) FROM SALES CUBE BY item, city, year Need compute the following Group-Bys: () (city) (date, product, customer), (date,product),(date, customer), (product, customer), (city, item) (date), (product), (customer) () (item) (city, year) (city, item, year) (year) (item, year) Data Warehouse Back-End Tools and Utilities Data extraction: get data from multiple, heterogeneous, and external sources Data cleaning: detect errors in the data and rectify them when possible Data transformation: convert data from legacy or host format to warehouse format Load: sort, summarize, consolidate, compute views, check integrity, and build indicies and partitions Refresh propagate the updates from the data sources to the warehouse Data Warehousing and OLAP Technology for Data Mining What is a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation Further development of data cube technology From data warehousing to data mining Data Warehouse Usage Three kinds of data warehouse applications Information processing • supports querying, basic statistical analysis, and reporting using crosstabs, tables, charts and graphs Analytical processing • multidimensional analysis of data warehouse data • supports basic OLAP operations, slice-dice, drilling, pivoting Data mining • knowledge discovery from hidden patterns • supports associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools. Differences among the three tasks From On-Line Analytical Processing to On Line Analytical Mining (OLAM) Why online analytical mining? High quality of data in data warehouses • DW contains integrated, consistent, cleaned data Available information processing structure surrounding data warehouses • ODBC, OLEDB, Web accessing, service facilities, reporting and OLAP tools OLAP-based exploratory data analysis • mining with drilling, dicing, pivoting, etc. On-line selection of data mining functions • integration and swapping of multiple mining functions, algorithms, and tasks. Architecture of OLAM An OLAM Architecture Mining query Mining result Layer4 User Interface User GUI API OLAM Engine OLAP Engine Layer3 OLAP/OLAM Data Cube API Layer2 MDDB MDDB Meta Data Filtering&Integration Database API Filtering Layer1 Data cleaning Databases Data Data integration Warehouse Data Repository Summary Data warehouse A subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process A multi-dimensional model of a data warehouse Star schema, snowflake schema, fact constellations A data cube consists of dimensions & measures OLAP operations: drilling, rolling, slicing, dicing and pivoting OLAP servers: ROLAP, MOLAP, HOLAP Efficient computation of data cubes Partial vs. full vs. no materialization Multiway array aggregation Bitmap index and join index implementations Further development of data cube technology Discovery-drive and multi-feature cubes From OLAP to OLAM (on-line analytical mining) Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining by Tan, Steinbach, Kumar Modified by Songrit Maneewongvatana & Suthep Madarasmi 57 Why Mine Data? Commercial Viewpoint 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? 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 4,000,000 3,500,000 The Data Gap 3,000,000 2,500,000 2,000,000 1,500,000 Total new disk (TB) since 1995 1,000,000 Number of analysts 500,000 0 1995 1996 1997 1998 1999 From: R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering Applications” What is Data Mining? Many Definitions Non-trivial extraction of implicit, previously unknown and potentially useful information from data Exploration & analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns. What is (not) Data Mining? What is NOT Data Mining? – Look up phone number in phone directory – Query a Web search engine for information about “Amazon” What is Data Mining – Certain names are more widespread 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,) Origins of Data Mining 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 Statistics/ AI Machine Learning/ Pattern Recognition 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 Clustering Association Rule Discovery Sequential Pattern Discovery Regression Deviation Detection Data mining Predictive Classification Descriptive Regression Deviation Detection Clustering Sequence Pattern Discovery Association rules 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 Tid Refund Marital Status Taxable Income Cheat Refund Marital Status Taxable Income Cheat 1 Yes Single 125K No No Single 75K ? 2 No Married 100K No Yes Married 50K ? 3 No Single 70K No No Married 150K ? 4 Yes Married 120K No Yes Divorced 90K ? 5 No Divorced 95K Yes No Single 40K ? 6 No Married No No Married 80K ? 60K 10 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 10 No Single 90K Yes Training Set Learn Classifier Test Set Model Neural Networks for Data Mining An actual neuron A crude model of a neuron Computational Neural Networks: A computational approach inspired by the architecture of the biological nervous system Synapse Dendrite Axon Soma Cat Neural Probe to Study Response pr obe pr obe cat neuron un lucky cat Neural Response RESPONSE milli Volts Shine Light Intensity STIMULUS Tim e Axis No Light oscillosco pe The Perceptron Model I1 I2 I3 I4 w1 Sum Threshold w2 w3 w4 O Example Weights: And & Or Problems I1 w1 >T I1 w1 + I w 2 2 O I2 w 2 I1 I2 0 0 1 1 0 1 0 1 0 0 0 1 0 1 1 1 1 1 0 0 w1 w2 1 1 1 1 -1 0 1.5 0.5 - 0.5 threshold AND OR NOT I1 I 1w1 I 2 w2 T I 1w1 I 2 w2 1 ( T ) 0 Weight Adjustments I1 I2 O Eqn: I1w 1 I 2 w 2 1 w 3 0 0 0 0 w1 0 w 2 1 w 3 0 w 3 0 0 1 0 0 w1 1 w 2 1 w 3 0 w 2 w 3 0 1 0 0 1 w1 0 w 2 1 w 3 0 w1 w 3 0 1 1 1 1 w1 1 w 2 1 w 3 0 w1 w 2 w 3 0 3-D and 2-D Plot of AND Table Problem is to find a plane that separates the “on” circle from the “off” circles. - Output is 0 - Output is 1 Training Procedure 1. First assign any values to w1, w2 and w3 2. Using the current weight values w1, w2 and w3 and the next training item inputs I1and I2 compute the value: V = I1w1 I2w2 1 w3 3. If V 0 set computed output C to 1 else set to 0. 4. If the computed output C is not the same as the current training item output O, Adjust Weights. 5. Repeat steps 2-4. If you run out of training items, start with the first training item. Stop repeating if no weight changes through 1 complete training cycle). Gradient Descent Algorithm w1 Next w1Current I 1( C O) w2 Next w 2 Current I 2 ( C O) w3 Next w 3Current ( C O) Linearly vs. Non-Linearly Separable The XOR Problem is Linearly Nonseparable The Back Propagation Model Five Four Three 6 input, 1 output 5 input, 1 output 4 input, 1 output Perceptr ons Perceptr ons Perceptr ons I1 I2 I3 I4 w1 Sum Threshold w2 O w3 w4 One Backprop Unit Input hid den Output layers Backpropagation Network Advantage of Backprop over Perceptron f e a t u r e 2 ย กก ย ย ย กก ถ หก ก ถ ก ก ก หหห ก กก ถถ ห ย ก ก ย ย ย กก ห กก กก ถถถ กก ห ก ห ห หก ถ feature 1 Input: Clu ster based Layer 1: Decision on two features bou ndaries draw n ย กก ย ย ย กก ห กก กก ถถถ ก ห กก ห ห ก ถ ห ย กก ย ย ย ก ห กกกกก ถถถ กก ห ก ห หห ก ถ Layer 2: Decision Layer 3: Decision regions determined ก regions ( ) grouped Backprop Learning Algorithm 1. Assign random values to all the weights 2. Choose a pattern from the training set (similar to perceptron). 3. Propagate the signal through to get final output (similar to perceptron). 4. Compute the error for the output layer (similar to the perceptron). 5. Compute the errors in the preceding layers by propagating the error backwards. 6. Change the weight between neuron A and each neuron B in another layer by an amount proportional to the observed output of B and the error of A. 7. Repeat step 2 for next training sample. Application: Needs Enough Training หห ห ก ก กก A small train ing set means many possible decisio n bou ndaries . ห ห หห หห ห ก หหหห ก กก กก กกก กก ก A large traini ng set constrain ts the decision boun dary more. Speech Recognition Data Mining using Neural Networks Stock Market Predictor N-Net 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 companyinteraction 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. 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 Category Total Correctly word filtering). Articles Placed Financial 555 364 Foreign 341 260 National 273 36 Metro 943 746 Sports 738 573 Entertainment 354 278 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. Can identify potential cross-selling opportunities TID Items 1 2 3 4 5 Bread, Coke, Milk Beer, Bread Beer, Coke, Diaper, Milk Beer, Bread, Diaper, Milk Coke, Diaper, Milk Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer} 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! 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