Clustering Undergraduate Computer Science Student Final Project Based on Frequent Itemset
Автор: Lusi Maulina Erman, Imas Sukaesih Sitanggang
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 11 Vol. 8, 2016 года.
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Abstract is a part of document has an important role in explaining the whole document. Words that frequently appear can be used as a reference in grouping the final project document into categories. Text mining method can be used to group the abstracts. The purpose of this study is to apply the method of association rule mining namely ECLAT algorithm to find most common terms combination and to group a collection of abstracts. The data used in this study is documents of final project abstract in English of undergraduate computer science student of IPB from 2012 to 2014. This research used stopwords about common computer science terminology, applied association rule mining with support of 0.1, 0.15, 0.2, 0.25, 0.3, and 0.35, and used k-Means clustering with number of cluster (k) of 10 because it gives the lowest SSE. This research compared the value of support, SSE, the number of cluster members, and purity value in each cluster. The best clustering result is data with additional stopwords and without applying association rule mining, and with k is 10. The SSE result is 23 485.03, and with purity of 0.512
Abstract, association rule mining, frequent itemset, K-Means, purity
Короткий адрес: https://sciup.org/15012583
IDR: 15012583
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