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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2010.12
- 수록면
- 1,353 - 1,359 (7page)
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초록· 키워드
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Basic objective in cluster analysis is to discover natural groupings of items. In general, clustering is conducted based on some similarity (or dissimilarity) matrix or the original input data. Various measures of similarities between objects are developed. In this paper, we consider a clustering of huge categorical real data set which shows the aspects of time-location-activity of Korean people. Some useful similarity measure for the data set, are developed and adopted for the categorical variables. Hierarchical and nonhierarchical clustering method are applied for the considered data set which is huge and consists of many categorical variables.
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참고문헌 (12)
참고문헌 신청
1. [학술지(정기간행물)] - Almeida, J. A. S. / 2007 / Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering / Chemometrics and Intelligent Laboratory Systems 87 : 208 ~ 217
2. [단행본] - Cox, T. F. / 2005 / An introduction to multivariate data analysis / Hodder Arnold
3. [단행본] - Dillon, W. R. / 1984 / Multivariate analysis: Methods and applications / John Wiley & Sons
4. [학술대회논문] - Huang Z. / 1997 / fast clustering algorithm to cluster very large categorical data sets in data mining / Proceedings of the SIGMOD workshop on research issues on data mining and knowledge discovery : 1 ~ 8
5. [학술지(정기간행물)] - Huang Z. / 1998 / Extensions to the k-means algorithm for clustering large data sets with categorical values / Data Mining Knowledge 2 (2) : 283 ~ 304
2. [단행본] - Cox, T. F. / 2005 / An introduction to multivariate data analysis / Hodder Arnold
3. [단행본] - Dillon, W. R. / 1984 / Multivariate analysis: Methods and applications / John Wiley & Sons
4. [학술대회논문] - Huang Z. / 1997 / fast clustering algorithm to cluster very large categorical data sets in data mining / Proceedings of the SIGMOD workshop on research issues on data mining and knowledge discovery : 1 ~ 8
5. [학술지(정기간행물)] - Huang Z. / 1998 / Extensions to the k-means algorithm for clustering large data sets with categorical values / Data Mining Knowledge 2 (2) : 283 ~ 304
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UCI(KEPA) : I410-ECN-0101-2018-041-001380407