Overview of Data Mining

Overview of Data Mining Meeting of WP Data Mining April 28, 2008 Bowo Prasetyo http://www.scribd.com/prazjp http://www.slideshare.net/bowoprasetyo ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Contents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

What Is Data Mining? ,[object Object],1) Berry and Linoff, Data Mining Techniques for Marketing, Sales and Customer Support (Book), 1997

Does It Differ To Statistics? ,[object Object],16) D. Pregibon, Data Mining: Statistical Computing and Graphics , p. 7-8, 1997 Statistics Artificial Intelligence Database Data Mining

Statistics, AI, Database ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Why Uses Data Mining? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],2) ESG Research, New ESG Research Finds Large Organizations Experiencing Explosive Growth in Log Data Collection, Analysis, and Storage , 2007 ( http://www.enterprisestrategygroup.com/_documents/NewsEvent/NewsEvent439.pdf ) 3) EMC — IDC Research, The Expanding Digital Universe: A Forecast of Worldwide Information Growth Through 2010 , 2006 ( http://www.emc.com/about/destination/digital_universe/ )

What Can Data Mining Do? Examples

On Business and Network Security ,[object Object],[object Object],[object Object],[object Object],4) G. Adomavicius and A. Tuzhilin, Using data mining methods to build customer profiles , in Computer magazine p. 74-82, 2001 5) Z. Huang, H. Chen, C. Hsu, W. Chen, S. Wu, Credit rating analysis with support vector machines and neural networks: a market comparative study , in Journal of Decision Support Systems p. 543-558, 2004 6) T. Fawcett and F. Provost, Adaptive Fraud Detection , in Journal of Data Mining and Knowledge Discovery p. 291-316, 2004 7) W. Lee and S. J. Stolfo, Data Mining Approaches for Intrusion Detection , in Proceedings of the 7th USENIX Security Symposium, 1998

On The Web ,[object Object],[object Object],[object Object],[object Object],8) R. Cooley, B. Mobasher, J. Srivastava, Web Mining: Information and Pattern Discovery on the World Wide Web , in Proceedings of 9th International Conference on Tools with Artificial Intelligence (ICTAI) p. 0558, 1997 9) Larry Page, Sergey Brin, R. Motwani, T. Winograd, The PageRank Citation Ranking: Bringing Order to the Web , 1998 ( http://citeseer.ist.psu.edu/page98pagerank.html ) 10) M. Eirinaki and M. Vazirgiannis, Web mining for web personalization , in ACM Transactions on Internet Technology (TOIT) p. 1- 27, 2003. 11) S. W. Changchien and T. Lu, Mining association rules procedure to support on-line recommendation by customers and products fragmentation , in Journal of Expert Systems with Applications v. 20-4 p. 325-335, 2001

On Environment ,[object Object],[object Object],[object Object],[object Object],12) J. Han, K. Koperski, N. Stefanovic, GeoMiner: a system prototype for spatial data mining, in Proceedings of ACM SIGMOD international conference on Management of data p. 553 - 556, 1997 13) Z. Nazeri and J. Zhang, Mining aviation data to understand impacts of severe weather on airspace system performance , in Proceedings of International Conference on Coding and Computing p. 518- 523, 2002. 14) V. Kumar, M. Steinbach, P. Tan, S. Klooster, C. Potter, A. Torregrosa, Mining Scientific Data: Discovery of Patterns in the Global Climate System , in Proceedings of the Joint Statistical Meetings p. 5--9, 2001 15) M. Steinbach, P. Tan, V. Kumar, S. Klooster, C. Potter , Data Mining for the Discovery of Ocean Climate Indices , in Proceedings of the 5th Workshop on Scientific Data Mining p. 7-16, 2002

Methods in Data Mining Basic Methods

Classification, Clustering, Association Rules ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Classification ,[object Object],[object Object],[object Object],17) http ://en.wikipedia.org/wiki/Naive_Bayes_classifier

Clustering ,[object Object],[object Object],[object Object],[object Object],[object Object],18) J. A. Hartigan and M. A. Wong, A k-means clustering algorithm, in Applied Statistics, 28 (1) p. 100-108, 1979

Association Rules ,[object Object],[object Object],[object Object],[object Object],19) R. Agrawal, R. Srikant, Fast Algorithms for Mining Association Rules , in Proc. 20th Int. Conf. Very Large Data Bases, VLDB, 1994

Association Rules ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],( C k : Candidate itemset of size k ) ( L k : frequent itemset of size k whose support >= minsup )

Association Rules ,[object Object],[object Object],[object Object]

Visualization Of Mining Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Contoh Kasus Aturan Asosiasi di Toserba

Item dan Transaksi ,[object Object],[object Object],[object Object],[object Object],[object Object],transaksi item

Frequent Item (Item Sering) ,[object Object],[object Object],support minimum support

n -Length Item ( n -Item) ,[object Object],2-length item 3-length item

Aturan Asosiasi ,[object Object],[object Object],beras => minyak goreng support(minyak goreng & beras) support(beras) = 2/2 = 1 confidence antecedent consequent

Mining Environmental Data Examples

Explosion in Environmental Data ,[object Object],[object Object],[object Object],[object Object]

Geo-spatial Database ,[object Object],[object Object],[object Object],[object Object],[object Object],GeoMiner

Earth Science ,[object Object],Regions that are covered by the highly correlated pattern, FPAR-Hi  NPP-Hi Shrubland regions FPAR: Fractional Intercepted Photosynthetically Active Radiation NPP : Net Primary Production

Earth Science ,[object Object],Two clusters for NPP (land) and two clusters for SST (ocean). The clusters approximate the northern and southern hemispheres, for land and ocean. SST: sea surface temperature

Earth Science ,[object Object],Clusters of ocean near the Philipines (SST) and lands of Eastern Brazil, Southern Africa, and a bit of Australia (NPP) is highly correlated (0.47). In particular, this sea region is highly correlated (0.66), with SOI, which is a climate index related to El Niño, and it is known that parts of Southern Africa and Australia experience droughts related to El Nino.

Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object]

Overview of Data Mining

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Overview of Data Mining