Unsupervised clustering based on feature-value / instance transposition selection

Akira Kusaba, Takako Hashimoto, Kilho Shin, David Lawrence Shepard, Tetsuji Kuboyama

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

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.

本文言語英語
ホスト出版物のタイトル2020 IEEE Region 10 Conference, TENCON 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1192-1197
ページ数6
ISBN(電子版)9781728184555
DOI
出版ステータス出版済み - 11 16 2020
イベント2020 IEEE Region 10 Conference, TENCON 2020 - Virtual, Osaka, 日本
継続期間: 11 16 202011 19 2020

出版物シリーズ

名前IEEE Region 10 Annual International Conference, Proceedings/TENCON
2020-November
ISSN(印刷版)2159-3442
ISSN(電子版)2159-3450

会議

会議2020 IEEE Region 10 Conference, TENCON 2020
Country日本
CityVirtual, Osaka
Period11/16/2011/19/20

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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