Unsupervised clustering based on feature-value / instance transposition selection

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publication2020 IEEE Region 10 Conference, TENCON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728184555
Publication statusPublished - Nov 16 2020
Event2020 IEEE Region 10 Conference, TENCON 2020 - Virtual, Osaka, Japan
Duration: Nov 16 2020Nov 19 2020

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference2020 IEEE Region 10 Conference, TENCON 2020
CityVirtual, Osaka

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Unsupervised clustering based on feature-value / instance transposition selection'. Together they form a unique fingerprint.

Cite this