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Syllabus
Schedule
Professor
Software
Data
FYA

Clustering


Clustering refers to classifying items (cases or variables) into groups based on their similarities to others.
 

Topics

  • johnson's hierarchical

  • combinatorial optimization

Readings

  • < none >
 

Handouts

Data


Bibliography


  • Cowgill, G. L. 1968. Archaeological applications of factor, cluster, and proximity analysis. American Antiquity 33:367-375.
  • Abonyi, János& Feil, Balázs (2007). Cluster analysis for data mining and system identification. Boston and Basel, Switzerland: Birkhäuser Basel.
  • Aldenderfer, Mark S. and Roger K. Blashfield (1984). Cluster analysis. Thousand Oaks, CA: Sage Publications, Quantitative Applications in the Social Sciences Series No. 44.
  • Anderberg, M. R. (1973). Cluster analysis for applications. New York: Academic Press.
  • Arabie, P.; Carroll, J. D.; & DeSarbo, W. S. (1987). Three-way scaling and clustering. Beverly Hills, CA: Sage.
  • Corter, James E. (1996). Tree models of similarity and association. Thousand Oaks, CA: Sage Publications, Quantitative Applications in the Social Sciences Series No. 112.
  • Everitt B. S. (1980) Cluster Analysis,. London: Heinemann.
  • Everitt, B. S., & Rabe-Hesketh, S. (1997). The analysis of proximity data. London: Arnold.
  • Everitt, Brian S., Sabine Landau, & Morven Leese (2001). Cluster analysis, 4th Edition. London: Edward Arnold Publishers Ltd. Highly recommended introductory text.
  • Jain, A. K.& Dubey, R. C. (1988). Algorithms for clustering data. Englewood Cliffs, NJ: Prentice Hall.
  • Jajuga, Krzystof; Sokolowski; Andrzej; & Bock, Hans-Hermann (2002). Classification, clustering and data analysis. Y: Springer.
  • Kachigan, Sam K. (1982). Multivariate statistical analysis. NY: Radius Press. Chapter 8 provides a very readable introduction to cluster analysis.
  • Kaufman, Leonard & Rousseeuw Peter J. (2005). Finding groups in data: An introduction to cluster analysis NY: Wiley-Interscience.
  • Melia, M. & Heckerman, D. (1998). An experimental comparison of several clustering and initialization methods. Microsoft Research Technical Report MSR-TR-98-06.
  • Rapkin, B. D., & Luke, D. A. (1993). Cluster analysis in community research: Epistemology and practice. American Journal of Community Psychology 21, 247-277.
  • Romesburg, Charles (2004). Cluster analysis for researchers. Internet: lulu.com.
  • Sarle, W.S & Kuo, An-Hsiang (1993). The MODECLUS procedure. SAS Technical Report P-256, Cary, NC: SAS Institute Inc.
  • Schneider, Andreas & Roberts, A. E. (2005). Classification and the relations of meaning. Quality & Quantity 38(5): 547-557. Treats the logic of K-means cluster analysis in the classification of affective meanings:
  • Sireci, S. G. & Geisinger , K. F. (1992). Analyzing test content using cluster analysis and multidimensional scaling. Applied Psychological Measurement 16(1), 17-31.
  • Theodoridis, S. & Koutroumbas, K. (1999). Pattern recognition. NY: Academic Press.
  • Zhang, T.; Ramakrishnon, R.; & Livny, M. (1996). BIRCH: Method for very large databases. Proceedings of the ACM. Management of Data. Pp. 103–114. Montreal, Canada.
 

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