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