Jiawei Han and Micheline Kamber



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Jiawei Han and Micheline Kamber

  • Jiawei Han and Micheline Kamber

  • Department of Computer Science

  • University of Illinois at Urbana-Champaign

  • www.cs.uiuc.edu/~hanj

  • ©2006 Jiawei Han and Micheline Kamber. All rights reserved.


The Explosive Growth of Data: from terabytes to petabytes

  • The Explosive Growth of Data: from terabytes to petabytes

    • Data collection and data availability
      • Automated data collection tools, database systems, Web, computerized society
    • Major sources of abundant data
      • Business: Web, e-commerce, transactions, stocks, …
      • Science: Remote sensing, bioinformatics, scientific simulation, …
      • Society and everyone: news, digital cameras, YouTube
  • We are drowning in data, but starving for knowledge!

  • “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets



Where does the data come from?—Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies

  • Where does the data come from?—Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies

  • Target marketing

    • Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc.
    • Determine customer purchasing patterns over time
  • Cross-market analysis—Find associations/co-relations between product sales, & predict based on such association

  • Customer profiling—What types of customers buy what products (clustering or classification)

  • Customer requirement analysis

    • Identify the best products for different groups of customers
    • Predict what factors will attract new customers
  • Provision of summary information

    • Multidimensional summary reports
    • Statistical summary information (data central tendency and variation)


Approaches: Clustering & model construction for frauds, outlier analysis

  • Approaches: Clustering & model construction for frauds, outlier analysis

  • Applications: Health care, retail, credit card service, telecomm.

    • Auto insurance: ring of collisions
    • Money laundering: suspicious monetary transactions
    • Medical insurance
      • Professional patients, ring of doctors, and ring of references
      • Unnecessary or correlated screening tests
    • Telecommunications: phone-call fraud
      • Phone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm
    • Retail industry
      • Analysts estimate that 38% of retail shrink is due to dishonest employees
    • Anti-terrorism


1960s:

  • 1960s:

    • Data collection, database creation, IMS and network DBMS
  • 1970s:

    • Relational data model, relational DBMS implementation
  • 1980s:

    • Advanced data models (extended-relational, OO, deductive, etc.)
    • Application-oriented DBMS (spatial, temporal, multimedia, etc.)
  • 1990s:

    • Data mining, data warehousing, multimedia databases, and Web databases
  • 2000s

    • Stream data management and mining
    • Data mining and its applications
    • Web technology (XML, data integration) and global information systems


Data mining (knowledge discovery from data)

  • 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”?

    • Simple search and query processing
    • (Deductive) expert systems


Data mining—core of knowledge discovery process

    • Data mining—core of knowledge discovery process


Data in the real world is dirty

  • Data in the real world is dirty

    • incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data
      • e.g., occupation=“ ”
    • noisy: containing errors or outliers
      • e.g., Salary=“-10”
    • inconsistent: containing discrepancies in codes or names
      • e.g., Age=“42” Birthdate=“03/07/1997”
      • e.g., Was rating “1,2,3”, now rating “A, B, C”
      • e.g., discrepancy between duplicate records


Incomplete data may come from

  • Incomplete data may come from

    • “Not applicable” data value when collected
    • Different considerations between the time when the data was collected and when it is analyzed.
    • Human/hardware/software problems
  • Noisy data (incorrect values) may come from

  • Inconsistent data may come from

    • Different data sources
    • Functional dependency violation (e.g., modify some linked data)
  • Duplicate records also need data cleaning



No quality data, no quality mining results!

  • No quality data, no quality mining results!

    • Quality decisions must be based on quality data
      • e.g., duplicate or missing data may cause incorrect or even misleading statistics.
    • Data warehouse needs consistent integration of quality data
  • Data extraction, cleaning, and transformation comprises the majority of the work of building a data warehouse







Tremendous amount of data

  • Tremendous amount of data

    • Algorithms must be highly scalable to handle large amounts of data
  • High-dimensionality of data

    • Micro-array may have tens of thousands of dimensions
  • High complexity of data

    • Data streams and sensor data
    • Time-series data, temporal data, sequence data
    • Structure data, graphs, social networks and multi-linked data
    • Heterogeneous databases and legacy databases
    • Spatial, spatiotemporal, multimedia, text and Web data
  • New and sophisticated applications



General functionality

  • General functionality

    • Descriptive data mining
    • Predictive data mining
  • Different views lead to different classifications

    • Data view: Kinds of data to be mined
    • Knowledge view: Kinds of knowledge to be discovered
    • Method view: Kinds of techniques utilized
    • Application view: Kinds of applications adapted


Database-oriented data sets and applications

  • Database-oriented data sets and applications

    • Relational database, data warehouse, transactional database
  • Advanced data sets and advanced applications

    • Object-relational databases
    • Time-series data, temporal data, sequence data (incl. bio-sequences)
    • Spatial data and spatiotemporal data
    • Text databases and Multimedia databases
    • Data streams and sensor data
    • The World-Wide Web
    • Heterogeneous databases and legacy databases


Concept/class description:

  • Concept/class description:

    • Characterization: summarizing the data of the class under study in general terms
      • E.g. Characteristics of customers spending more than 10000 sek per year
    • Discrimination: comparing target class with other (contrasting) classes
      • E.g. Compare the characteristics of products that had a sales increase to products that had a sales decrease last year


Frequent patterns, association, correlations

  • Frequent patterns, association, correlations

    • Frequent itemset
    • Frequent sequential pattern
    • Frequent structured pattern
    • E.g. buy(X, “Diaper”)  buy(X, “Beer”) [support=0.5%, confidence=75%]
    • confidence: if X buys a diaper, then there is 75% chance that X buys beer
    • support: of all transactions under consideration 0.5% showed that diaper and
    • beer were bought together
    • E.g. Age(X, ”20..29”) and income(X, ”20k..29k”)  buys(X, ”cd-player”) [support=2%, confidence=60%]


Classification and prediction

  • Classification and prediction

    • Construct models (functions) that describe and distinguish classes or concepts for future prediction.
    • The derived model is based on analyzing training data – data whose class labels are known.
      • E.g., classify countries based on (climate), or classify cars based on (gas mileage)
    • Predict some unknown or missing numerical values


Cluster analysis

  • Cluster analysis

    • Class label is unknown: Group data to form new classes, e.g., cluster customers to find target groups for marketing
    • Maximizing intra-class similarity & minimizing interclass similarity
  • Outlier analysis

    • Outlier: Data object that does not comply with the general behavior of the data
    • Noise or exception? Useful in fraud detection, rare events analysis
  • Trend and evolution analysis

    • Trend and deviation


Data mining may generate thousands of patterns: Not all of them are interesting

  • Data mining may generate thousands of patterns: Not all of them are interesting

    • Suggested approach: Human-centered, query-based, focused mining
  • Interestingness measures

    • A pattern is interesting if it is easily understood by humans, valid on new or test data with some degree of certainty, potentially useful, novel, or validates some hypothesis that a user seeks to confirm
  • Objective vs. subjective interestingness measures

    • Objective: based on statistics and structures of patterns, e.g., support, confidence, etc.
    • Subjective: based on user’s belief in the data, e.g., unexpectedness, novelty, actionability, etc.


Find all the interesting patterns: Completeness

  • Find all the interesting patterns: Completeness

    • Can a data mining system find all the interesting patterns? Do we need to find all of the interesting patterns?
    • Heuristic vs. exhaustive search
    • Association vs. classification vs. clustering
  • Search for only interesting patterns: An optimization problem

    • Can a data mining system find only the interesting patterns?
    • Approaches
      • First generate all the patterns and then filter out the uninteresting ones
      • Generate only the interesting patterns—mining query optimization




Classification

  • Classification

    • #1. C4.5: Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann., 1993.
    • #2. CART: L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, 1984.
    • #3. K Nearest Neighbours (kNN): Hastie, T. and Tibshirani, R. 1996. Discriminant Adaptive Nearest Neighbor Classification. TPAMI. 18(6)
    • #4. Naive Bayes Hand, D.J., Yu, K., 2001. Idiot's Bayes: Not So Stupid After All? Internat. Statist. Rev. 69, 385-398.
  • Statistical Learning

    • #5. SVM: Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. Springer-Verlag.
    • #6. EM: McLachlan, G. and Peel, D. (2000). Finite Mixture Models. J. Wiley, New York. Association Analysis
    • #7. Apriori: Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. In VLDB '94.
    • #8. FP-Tree: Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns without candidate generation. In SIGMOD '00.


Link Mining

  • Link Mining

    • #9. PageRank: Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual Web search engine. In WWW-7, 1998.
    • #10. HITS: Kleinberg, J. M. 1998. Authoritative sources in a hyperlinked environment. SODA, 1998.
  • Clustering

    • #11. K-Means: MacQueen, J. B., Some methods for classification and analysis of multivariate observations, in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, 1967.
    • #12. BIRCH: Zhang, T., Ramakrishnan, R., and Livny, M. 1996. BIRCH: an efficient data clustering method for very large databases. In SIGMOD '96.
  • Bagging and Boosting

    • #13. AdaBoost: Freund, Y. and Schapire, R. E. 1997. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 1 (Aug. 1997), 119-139.


Sequential Patterns

  • Sequential Patterns

    • #14. GSP: Srikant, R. and Agrawal, R. 1996. Mining Sequential Patterns: Generalizations and Performance Improvements. In Proceedings of the 5th International Conference on Extending Database Technology, 1996.
    • #15. PrefixSpan: J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and M-C. Hsu. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In ICDE '01.
  • Integrated Mining

    • #16. CBA: Liu, B., Hsu, W. and Ma, Y. M. Integrating classification and association rule mining. KDD-98.
  • Rough Sets

    • #17. Finding reduct: Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, 1992
  • Graph Mining

    • #18. gSpan: Yan, X. and Han, J. 2002. gSpan: Graph-Based Substructure Pattern Mining. In ICDM '02.


#1: C4.5 (61 votes)

  • #1: C4.5 (61 votes)

  • #2: K-Means (60 votes)

  • #3: SVM (58 votes)

  • #4: Apriori (52 votes)

  • #5: EM (48 votes)

  • #6: PageRank (46 votes)

  • #7: AdaBoost (45 votes)

  • #7: kNN (45 votes)

  • #7: Naive Bayes (45 votes)

  • #10: CART (34 votes)



1989 IJCAI Workshop on Knowledge Discovery in Databases

  • 1989 IJCAI Workshop on Knowledge Discovery in Databases

    • Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991)
  • 1991-1994 Workshops on Knowledge Discovery in Databases

    • Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 1996)
  • 1995-1998 International Conferences on Knowledge Discovery in Databases and Data Mining (KDD’95-98)

    • Journal of Data Mining and Knowledge Discovery (1997)
  • ACM SIGKDD conferences since 1998 and SIGKDD Explorations

  • More conferences on data mining

    • PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc.
  • ACM Transactions on KDD starting in 2007



KDD Conferences

  • KDD Conferences

    • ACM SIGKDD Int. Conf. on Knowledge Discovery in Databases and Data Mining (KDD)
    • SIAM Data Mining Conf. (SDM)
    • (IEEE) Int. Conf. on Data Mining (ICDM)
    • Conf. on Principles and practices of Knowledge Discovery and Data Mining (PKDD)
    • Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD)


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