Introduction to Data Mining

1. Introduction to Data Mining Donghui Zhang CCIS, Northeastern University Data Mining: Concepts and Techniques

2. http://www.cs.uiuc.edu/~hanj The current talk slide was extracted and modified from Dr. Han’s lecture slides. Data Mining: Concepts and Techniques

3. Motivation • Data explosion problem • Automated data collection tools and mature database technology lead to tremendous amounts of data accumulated and/or to be analyzed in databases, data warehouses, and other information repositories • We are drowning in data, but starving for knowledge! • Solution: Data warehousing and data mining • Data warehousing and on-line analytical processing • Mining interesting knowledge (rules, regularities, patterns, constraints) from data in large databases Data Mining: Concepts and Techniques

4. Evolution of Database Technology • 1960s: • Data collection, database creation, IMS and network DBMS • 1970s: • Relational data model, relational DBMS implementation • 1980s: • RDBMS, advanced data models (extended-relational, OO, deductive, etc.) • Application-oriented DBMS (spatial, scientific, engineering, etc.) • 1990s: • Data mining, data warehousing, multimedia databases, and Web databases • 2000s • Stream data management and mining • Data mining with a variety of applications • Web technology and global information systems Data Mining: Concepts and Techniques

5. Data Mining: Confluence of Multiple Disciplines Database Systems Statistics Data Mining Machine Learning Visualization Algorithm Other Disciplines Data Mining: Concepts and Techniques

6. What Is Data Mining? • Data mining (knowledge discovery from data) • Extraction of interesting (non-trivial,implicit, previously unknown and potentially useful)patterns or knowledge from huge amount of data • Data mining: a misnomer? • Alternative names • Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. • Watch out: Is everything “data mining”? • (Deductive) query processing. • Expert systems or small ML/statistical programs Data Mining: Concepts and Techniques

7. Why Data Mining?—Potential Applications • Data analysis and decision support • Market analysis and management • Target marketing, customer relationship management (CRM), market basket analysis, cross selling, market segmentation • Risk analysis and management • Forecasting, customer retention, improved underwriting, quality control, competitive analysis • Fraud detection and detection of unusual patterns (outliers) • Other Applications • Text mining (news group, email, documents) and Web mining • Stream data mining • DNA and bio-data analysis Data Mining: Concepts and Techniques

8. Data Mining: A KDD Process Knowledge • Data mining—core of knowledge discovery process Pattern Evaluation Data Mining Task-relevant Data Selection Data Warehouse Data Cleaning Data Integration Databases Data Mining: Concepts and Techniques

9. Steps of a KDD Process • Learning the application domain • relevant prior knowledge and goals of application • Creating a target data set: data selection • Data cleaning and preprocessing: (may take 60% of effort!) • Data reduction and transformation • Find useful features, dimensionality/variable reduction, invariant representation. • Choosing functions of data mining • summarization, classification, regression, association, clustering. • Choosing the mining algorithm(s) • Data mining: search for patterns of interest • Pattern evaluation and knowledge presentation • visualization, transformation, removing redundant patterns, etc. • Use of discovered knowledge Data Mining: Concepts and Techniques

10. Architecture: Typical Data Mining System Graphical user interface Pattern evaluation Data mining engine Knowledge-base Database or data warehouse server Filtering Data cleaning & data integration Data Warehouse Databases Data Mining: Concepts and Techniques

11. Data Mining: On What Kinds of Data? • Relational database • Data warehouse • Transactional database • Advanced database and information repository • Object-relational database • Spatial and temporal data • Time-series data • Stream data • Multimedia database • Heterogeneous and legacy database • Text databases & WWW Data Mining: Concepts and Techniques

12. Data Mining Functionalities • Concept description: Characterization and discrimination • Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet regions • Association (correlation and causality) • Diaper à Beer [0.5%, 75%] • Classification and Prediction • Construct models (functions) that describe and distinguish classes or concepts for future prediction • E.g., classify countries based on climate, or classify cars based on gas mileage • Presentation: decision-tree, classification rule, neural network • Predict some unknown or missing numerical values Data Mining: Concepts and Techniques

13. Data Mining Functionalities (2) • Cluster analysis • Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns • Maximizing intra-class similarity & minimizing interclass similarity • Mining complex types of data Data Mining: Concepts and Techniques

14. 1. Concept Description • Descriptive vs. predictive data mining • Descriptive mining: describes concepts or task-relevant data sets in concise, summarative, informative, discriminative forms • Predictive mining: Based on data and analysis, constructs models for the database, and predicts the trend and properties of unknown data • Concept description: • Characterization: provides a concise and succinct summarization of the given collection of data • Comparison: provides descriptions comparing two or more collections of data

15. Class Characterization: An Example Initial Relation Prime Generalized Relation

16. Customer buys both Customer buys diaper Customer buys beer 2. Frequent Patterns and Association Rules • Itemset X={x1, …, xk} • Find all the rules XYwith min confidence and support • support, s, probability that a transaction contains XY • confidence, c,conditional probability that a transaction having X also contains Y. • Let min_support = 50%, min_conf = 50%: • A  C (50%, 66.7%) • C  A (50%, 100%) Data Mining: Concepts and Techniques

17. Apriori: A Candidate Generation-and-test Approach • Any subset of a frequent itemset must be frequent • if {beer, diaper, nuts} is frequent, so is {beer, diaper} • Every transaction having {beer, diaper, nuts} also contains {beer, diaper} • Apriori pruning principle: If there is any itemset which is infrequent, its superset should not be generated/tested! • Method: • generate length (k+1) candidate itemsets from length k frequent itemsets, and • test the candidates against DB Data Mining: Concepts and Techniques

18. The Apriori Algorithm—An Example Database TDB L1 C1 1st scan C2 C2 L2 2nd scan L3 C3 3rd scan Data Mining: Concepts and Techniques

19. Sequential Pattern Mining • Given a set of sequences, find the complete set of frequent subsequences A sequence : < (ef) (ab) (df) c b > A sequence database An element may contain a set of items. Items within an element are unordered and we list them alphabetically. <a(bc)dc> is a subsequence of <a(abc)(ac)d(cf)> Given support thresholdmin_sup =2, <(ab)c> is a sequential pattern Data Mining: Concepts and Techniques

20. 3. Classification & Prediction • Classification: • predicts categorical class labels (discrete or nominal) • classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data • Prediction: • models continuous-valued functions, i.e., predicts unknown or missing values • Typical Applications • credit approval • target marketing • medical diagnosis • treatment effectiveness analysis Data Mining: Concepts and Techniques

21. Training Dataset This follows an example from Quinlan’s ID3 Data Mining: Concepts and Techniques

22. Output: A Decision Tree for “buys_computer” age? <=30 overcast >40 30..40 student? credit rating? yes no yes fair excellent no yes no yes Data Mining: Concepts and Techniques

23. Algorithm for Decision Tree Induction • Basic algorithm (a greedy algorithm) • Tree is constructed in a top-down recursive divide-and-conquer manner • At start, all the training examples are at the root • Attributes are categorical (if continuous-valued, they are discretized in advance) • Examples are partitioned recursively based on selected attributes • Test attributes are selected on the basis of a heuristic or statistical measure (e.g., information gain) • Conditions for stopping partitioning • All samples for a given node belong to the same class • There are no remaining attributes for further partitioning – majority voting is employed for classifying the leaf • There are no samples left Data Mining: Concepts and Techniques

24. Other Classification Techniques • Classification by decision tree induction • Bayesian Classification • Classification by Neural Networks • Classification by Support Vector Machines (SVM) • Classification based on concepts from association rule mining Data Mining: Concepts and Techniques

25. 4. Cluster Analysis • Cluster: a collection of data objects • Similar to one another within the same cluster • Dissimilar to the objects in other clusters • Cluster analysis • Grouping a set of data objects into clusters • Clustering is unsupervised classification: no predefined classes • Typical applications • As a stand-alone tool to get insight into data distribution • As a preprocessing step for other algorithms

26. What Is Good Clustering? • A good clustering method will produce high quality clusters with • high intra-class similarity • low inter-class similarity • The quality of a clustering result depends on both the similarity measure used by the method and its implementation. • The quality of a clustering method is also measured by its ability to discover some or all of the hidden patterns. Data Mining: Concepts and Techniques

27. Major Clustering Approaches • Partitioning algorithms: Construct various partitions and then evaluate them by some criterion • Hierarchy algorithms: Create a hierarchical decomposition of the set of data (or objects) using some criterion • Density-based: based on connectivity and density functions • Grid-based: based on a multiple-level granularity structure • Model-based: A model is hypothesized for each of the clusters and the idea is to find the best fit of that model to each other Data Mining: Concepts and Techniques

28. The K-Means Partitioning Algorithm • Given k, the k-means algorithm is implemented in four steps: • Partition objects into k nonempty subsets • Compute seed points as the centroids of the clusters of the current partition (the centroid is the center, i.e., mean point, of the cluster) • Assign each object to the cluster with the nearest seed point • Go back to Step 2, stop when no more new assignment Data Mining: Concepts and Techniques

29. 5. Mining Complex Types of Data • Mining spatial databases • Mining multimedia databases • Mining time-series and sequence data • Mining stream data • Mining text databases • Mining the World-Wide Web Data Mining: Concepts and Techniques

30. E.g. Mining Time-Series: two tasks Time-series plot Data Mining: Concepts and Techniques

31. Task one: Trend analysis • Predict whether increase or decrease • Long-term or trend movements (trend curve) • Cyclic movements or cycle variations, e.g., business cycles • Seasonal movements or seasonal variations • i.e, almost identical patterns that a time series appears to follow during corresponding months of successive years. • Irregular or random movements Data Mining: Concepts and Techniques

32. Task two: Similarity Search • Normal database query finds exact match • Similarity search finds data sequences that differ only slightly from the given query sequence • Two categories of similarity queries • find a sequence that is similar to the query sequence • find all pairs of similar sequences Data Mining: Concepts and Techniques

33. Data Warehouse Data Mining: Concepts and Techniques

34. 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 asubject-oriented, integrated, time-variant, and nonvolatilecollection of data in support of management’s decision-making process.”—W. H. Inmon • Data warehousing: • The process of constructing and using data warehouses Data Mining: Concepts and Techniques

35. 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 Data Mining: Concepts and Techniques

36. item time item_key item_name brand type supplier_type time_key day day_of_the_week month quarter year location branch location_key street city state_or_province country branch_key branch_name branch_type Example of Star Schema Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures Data Mining: Concepts and Techniques

37. supplier item time item_key item_name brand type supplier_key supplier_key supplier_type time_key day day_of_the_week month quarter year city location branch location_key street city_key city_key city state_or_province country branch_key branch_name branch_type Example of Snowflake Schema Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures Data Mining: Concepts and Techniques

38. item time item_key item_name brand type supplier_type time_key day day_of_the_week month quarter year location location_key street city province_or_state country shipper branch shipper_key shipper_name location_key shipper_type branch_key branch_name branch_type Example of Fact Constellation Shipping Fact Table time_key Sales Fact Table item_key time_key shipper_key item_key from_location branch_key to_location location_key dollars_cost units_sold units_shipped dollars_sold avg_sales Measures Data Mining: Concepts and Techniques

39. Multidimensional Data • Sales volume as a function of product, month, and region Dimensions: Product, Location, Time Hierarchical summarization paths Region Industry Region Year Category Country Quarter Product City Month Week Office Day Product Month Data Mining: Concepts and Techniques

40. Cuboids & Cube all 0-D(apex) cuboid region product month 1-D cuboids product, month product, region month, region 2-D cuboids 3-D(base) cuboid product, month, region Data Mining: Concepts and Techniques

41. OLAP Server Architectures • Relational OLAP (ROLAP) • Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middle ware to support missing pieces • Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services • greater scalability • Multidimensional OLAP (MOLAP) • Array-based multidimensional storage engine (sparse matrix techniques) • fast indexing to pre-computed summarized data • Hybrid OLAP (HOLAP) • User flexibility, e.g., low level: relational, high-level: array • Specialized SQL servers • specialized support for SQL queries over star/snowflake schemas Data Mining: Concepts and Techniques

42. 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 Mining: Concepts and Techniques

43. Summary • Data mining: discovering interesting patterns from large amounts of data • A natural evolution of database technology, in great demand, with wide applications • Data mining functionalities: characterization, association, classification, clustering, mining complex data, etc. • Data warehousing Data Mining: Concepts and Techniques

44. Where to Find Data Mining Papers • Data mining and KDD (SIGKDD: CDROM) • Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc. • Journal: Data Mining and Knowledge Discovery, KDD Explorations • Database systems (SIGMOD: CD ROM) • Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA • Journals: ACM-TODS, IEEE-TKDE, JIIS, J. ACM, etc. • AI & Machine Learning • Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), etc. • Journals: Machine Learning, Artificial Intelligence, etc. • Statistics • Conferences: Joint Stat. Meeting, etc. • Journals: Annals of statistics, etc. • Visualization • Conference proceedings: CHI, ACM-SIGGraph, etc. • Journals: IEEE Trans. visualization and computer graphics, etc. Data Mining: Concepts and Techniques