L. Kaufmann and P. J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley, 1990. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
ADMIT: Anomaly-based Data Mining for Intrusions - Sequeira, Zaki (Correct)
....vi 1 vi 1 , ps eaf, vi 1 , ls a 1 ps eaf, vi 1 , ls a 1 , rm i 1 1 , ls a 1 , rm i 1 , vi 2 a 1 , rm i 1 , vi 2 , ps ef 3.1.2 Clustering User Sequences Once tokens have been converted into sequences, we next cluster them using a suitable algorithm. K Means [8] is an often favored clustering algorithm because it allows reallocation of samples even after assignment and it converges quickly. During each iteration, k means first assigns each point to the closest cluster center and then recalculates the cluster centers. The first step takes time O(#kN ) ....
L. Kaufmann, P.J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley and Sons. March 1990.
Clustering Large Datasets in Arbitrary Metric Spaces - Ganti, Ramakrishnan.. (1998) (27 citations) (Correct)
....times super linear in the size of the dataset. Therefore, they do not scale to large databases. Recently, clustering has received attention as an important data mining problem [8, 9, 10, 17, 21, 26] CLARANS [21] is a medoid based method which is more efficient than earlier medoid based algorithms [18], but has two drawbacks: it assumes that all objects fit in main memory, and the result is very sensitive to the input order [26] Techniques to improve CLARANS s ability to deal with diskresident datasets by focussing only on relevant parts of the database using R trees were also proposed [9, ....
....(k jOj) and a function f : O 7 R k such that f is an R k distance preserving transformation. For example, three objects x; y; z with the inter object distance distribution [d(x; y) 3; d(y; z) 4; d(z; x) 3 The medoid O k of a set of objects O is sometimes used as a cluster center [18]. It is defined as the object O m 2O that minimizes the average dissimilarity to all objects in O (i.e. P n i=1 d(O i ; O) is minimum when O = O m ) But, it is not possible to motivate the heuristic maintenance a la clustroid of the medoid. However, we expect similar heuristics to ....
L. Kaufmann and P. Rousseuw. Finding Groups in Data - An Introduction to Cluster Analysis. Wiley series in Probability and Mathematical Statistics, 1990.
The Analysis and Applications of Adaptive-Binning Color Histograms - Leow, Li (Correct)
No context found.
L. Kaufmann and P. J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley, 1990.
Clustering Classifiers for Knowledge Discovery from.. - Tsoumakas, Angelis.. (2004) (Correct)
No context found.
Leonard Kaufmann and Peter J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley Interscience, 1990.
Clustering Large Datasets in Arbitrary Metric Spaces - Venkatesh Ganti Raghu (1999) (27 citations) (Correct)
No context found.
L. Kaufmann and P. Rousseuw. Finding Groups in Data - An Introduction to Cluster Analysis. Wiley series in Probability and Mathematical Statistics, 1990.
Enhanced Biclustering on Expression Data - Yang, Wang, Wang, Yu (2003) (3 citations) (Correct)
No context found.
Kaufmann, L. and Rousseuw, P. (1990) Finding groups in data -- an introduction to cluster analysis, Wiley series in Probability and Mathematical Statistics.
delta-Clusters: Capturing Subspace Correlation in a Large.. - Yang, Wang, Wang, Yu (2002) (3 citations) (Correct)
No context found.
L. Kaufmann and P. Rousseuw, Finding groups in data --- an introduction to cluster analysis, Wiley series in Probability and Mathematical Statistics, 1990.
delta-Clusters: Capturing Subspace Correlation in a Large.. - Yang, Wang, Wang, Yu (2002) (3 citations) (Correct)
No context found.
L. Kaufmann and P. Rousseuw, Finding groups in data --- an introduction to cluster analysis, Wiley series in Probability and Mathematical Statistics, 1990.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC