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

Recommended

Recommended

More Related Content

What's hot

What's hot (9)

Similar to Overview of Data Mining

Similar to Overview of Data Mining (20)

More from Bowo Prasetyo

More from Bowo Prasetyo (10)

Recently uploaded

Recently uploaded (20)

Overview of Data Mining