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A Data Clustering Algorithm for Stra...

Sahoo, Ajit Kumar.

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A Data Clustering Algorithm for Stratified Data Partitioning in Artificial Neural Network.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A Data Clustering Algorithm for Stratified Data Partitioning in Artificial Neural Network./
作者:	Sahoo, Ajit Kumar.
面頁冊數:	114 p.
附註:	Source: Masters Abstracts International, Volume: 49-04, page: .
Contained By:	Masters Abstracts International49-04.
標題:	Engineering, Computer. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR70891
ISBN:	9780494708910

A Data Clustering Algorithm for Stratified Data Partitioning in Artificial Neural Network.
Sahoo, Ajit Kumar.

A Data Clustering Algorithm for Stratified Data Partitioning in Artificial Neural Network. - 114 p.

Source: Masters Abstracts International, Volume: 49-04, page: .

Thesis (M.Sc.)--University of Alberta (Canada), 2011.

The statistical properties of training, validation and test data play an important role in assuring optimal performance in artificial neural networks (ANN). Researchers have proposed randomized data partitioning (RDP) and stratified data partitioning (SDP) methods for partition of input data into training, validation and test datasets. RDP methods based on genetic algorithm (GA) are computationally expensive as the random search space can be in the power of twenty or more for an average sized dataset. For SDP methods, clustering algorithms such as self organizing map (SOM) and fuzzy clustering (FC) are used to form strata. It is assumed that data points in any individual stratum are in close statistical agreement. Reported clustering algorithms are designed to form natural clusters. In the case of large multivariate datasets, some of these natural clusters can be big enough such that the furthest data vectors are statistically far away from the mean. Further, these algorithms are computationally expensive as well. Here a custom design clustering algorithm (CDCA) has been proposed to overcome these shortcomings. Comparisons have been made using three benchmark case studies, one each from classification, function approximation and prediction domain respectively. The proposed CDCA data partitioning method was evaluated in comparison with SOM, FC and GA based data partitioning methods. It was found that the CDCA data partitioning method not only performed well but also reduced the average CPU time.

ISBN: 9780494708910Subjects--Topical Terms:

1669061
Engineering, Computer.

A Data Clustering Algorithm for Stratified Data Partitioning in Artificial Neural Network.
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520 $a The statistical properties of training, validation and test data play an important role in assuring optimal performance in artificial neural networks (ANN). Researchers have proposed randomized data partitioning (RDP) and stratified data partitioning (SDP) methods for partition of input data into training, validation and test datasets. RDP methods based on genetic algorithm (GA) are computationally expensive as the random search space can be in the power of twenty or more for an average sized dataset. For SDP methods, clustering algorithms such as self organizing map (SOM) and fuzzy clustering (FC) are used to form strata. It is assumed that data points in any individual stratum are in close statistical agreement. Reported clustering algorithms are designed to form natural clusters. In the case of large multivariate datasets, some of these natural clusters can be big enough such that the furthest data vectors are statistically far away from the mean. Further, these algorithms are computationally expensive as well. Here a custom design clustering algorithm (CDCA) has been proposed to overcome these shortcomings. Comparisons have been made using three benchmark case studies, one each from classification, function approximation and prediction domain respectively. The proposed CDCA data partitioning method was evaluated in comparison with SOM, FC and GA based data partitioning methods. It was found that the CDCA data partitioning method not only performed well but also reduced the average CPU time.
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