The K-Means clustering architecture in the multi-stage data mining process

Lecture Notes in Computer Science

Volumn 3481, Issue II, 2005, Pages 71-81

  Gerardo, Bobby D ^a   Lee, Jae Wan ^a   Choi, Yeon Sung ^a   Lee, Malrey ^b  

^a Kunsan National University   (South Korea)

^b Chonbuk National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER ARCHITECTURE; DATA MINING; DATA REDUCTION; DATA STRUCTURES; DECISION MAKING; HIERARCHICAL SYSTEMS; MATHEMATICAL MODELS;

ASSOCIATION RULES; CLUSTERING ARCHITECTURE; CLUSTERING MODELS; DATASETS;

SOFTWARE ENGINEERING;

EID: 24944568590     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11424826_8     Document Type: Conference Paper

References (8)

1. th Edition. USA: McGraw-Hill (2001).
2. Han J. & Kamber M.: Data Mining Concepts & Techniques. USA: Morgan Kaufmann (2001).
3. Chen, B., Haas, P., and Scheuermann, P.: A new two-phase sampling based algorithm for discovering association rules. Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2002.
4. Cluster Analysis defined: Available at: http://www.clustan.com/ what_is_cluster_analysis.html
5. Determining the Number of Clusters.: Available at: http://cgm.cs.mcgill.ca/~soss/cs644/projects/siourbas/cluster.html# kmeans.
6. Using Hierarchical Clustering in XLMiner.: Available at: http://www.resample.com/xlminer/help/HClst/HClst_intro.htm.
7. Agglomerative Hierarchical Clustering.: Available at http://www2.cs.uregina.ca/~hamilton/courses/831/notes/clustering/clustering.htm.
8. Ertz L., Steinbach M., & Kumar V. Finding Topics in Collections of Documents: A Shared Nearest Neighbor Approach. Text Mine '01, Workshop on Text Mining, First SIAM International Conference on Data Mining, Chicago, IL, (2001).