論文

査読有り
2020年

Unsupervised Clustering based on Feature-value / Instance Transposition Selection.

2020 IEEE Region 10 Conference(TENCON)
  • Akira Kusaba
  • ,
  • Takako Hashimoto
  • ,
  • Kilho Shin
  • ,
  • David Lawrence Shepard
  • ,
  • Tetsuji Kuboyama

2020-November
開始ページ
1192
終了ページ
1197
記述言語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1109/TENCON50793.2020.9293922
出版者・発行元
IEEE

This paper presents FITS, or Feature-value / Instance Transposition Selection, a method for unsupervised clustering. FITS is a tractable, explicable clustering method, which leverages the unsupervised feature value selection algorithm known as UFVS in the literature. FITS combines repeated rounds of UFVS with alternating steps of matrix transposition to produce a set of homogenous clusters that describe data well. By repeatedly swapping the role of feature and instance and applying the same selection process to them, FITS leverages UFVS's speed and can perform clustering in our experiments in tens milliseconds for datasets of thousands of features and thousands of instances.We performed feature selection-based clustering on two real-world data sets. One is aimed at topic extraction from Twitter data, and the other is aimed at gaining awareness of energy conservation from time-series power consumption data. This study also proposes a novel method based on iterative feature extraction and transposition. The effectiveness of this method is shown in an application of Twitter data analysis. On the other hand, a more straightforward use of feature selection is adopted in the application of time series power consumption data analysis.

リンク情報
DOI
https://doi.org/10.1109/TENCON50793.2020.9293922
DBLP
https://dblp.uni-trier.de/rec/conf/tencon/KusabaHSSK20
URL
https://dblp.uni-trier.de/rec/conf/tencon/2020
URL
https://dblp.uni-trier.de/db/conf/tencon/tencon2020.html#KusabaHSSK20
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85098936692&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85098936692&origin=inward
ID情報
  • DOI : 10.1109/TENCON50793.2020.9293922
  • ISSN : 2159-3442
  • eISSN : 2159-3450
  • ISBN : 9781728184555
  • DBLP ID : conf/tencon/KusabaHSSK20
  • SCOPUS ID : 85098936692

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