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Your search keyword '"cluster number"' showing total 3 results
Start Over Descriptor "cluster number" Topic data mining
3 results on '"cluster number"'

1. A Data Mining Application on Air Temperature Database

Author

Bilgin, T. Tugay, Çamurcu, A. Yılmaz, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Yakhno, Tatyana, editor

Published
2005
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2. On Rival Penalization Controlled Competitive Learning for Clustering with Automatic Cluster Number Selection.

Author

Yiu-ming Cheung

Subjects
*LEARNING, *ALGORITHMS, *DATA mining, *KNOWLEDGE management, *GAUSSIAN distribution, *COMPUTER programming
Abstract

The existing Rival Penalized Competitive Learning (RPCL) algorithm and its variants have provided an attractive way to perform data clustering without knowing the exact number of clusters. However, their performance is sensitive to the preselection of the rival delearning rate. In this paper, we further investigate the RPCL and present a mechanism to control the strength of rival penalization dynamically. Consequently, we propose the Rival Penalization Controlled Competitive Learning (RPCCL) algorithm and its stochastic version. In each of these algorithms, the selection of the delearning rate is circumvented using a novel technique. We compare the performance of RPCCL to RPCL in Gaussian mixture clustering and color image segmentation, respectively. The experiments have produced the promising results. [ABSTRACT FROM AUTHOR]

Published
2005
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3. The Improved Partition Coefficient

Author

Chun sheng Li

Subjects
Index (economics), Fuzzy clustering, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, General Medicine, computer.software_genre, partition coefficient, cluster number, Data set, Partition coefficient, Determining the number of clusters in a data set, Statistics, Cluster (physics), Data_FILES, fuzzy clustering, Data mining, computer, Engineering(all), Mathematics
Abstract

This paper proposed an improved partition coefficient (IPC) index by turning two disadvantages of partition coefficient (PC) index into advantages. Comparisons between IPC index and PC index and two existed cluster validity indexes are conducted on four real data sets. Experimental results show that IPC is able to identify the cluster number underlying the data set in the case that PC index is unable to do and outperforms the two existed cluster validity indexes.

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