Optimized combinatorial clustering for stochastic processes

Jumi Kim, Wookey Lee, Justin Jongsu Song, Soo Bok Lee

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)


As a new data processing era like Big Data, Cloud Computing, and Internet of Things approaches, the amount of data being collected in databases far exceeds the ability to reduce and analyze these data without the use of automated analysis techniques, data mining. As the importance of data mining has grown, one of the critical issues to emerge is how to scale data mining techniques to larger and complex databases so that it is particularly imperative for computationally intensive data mining tasks such as identifying natural clusters of instances. In this paper, we suggest an optimized combinatorial clustering algorithm for noisy performance which is essential for large data with random sampling. The algorithm outperforms conventional approaches through various numerical and qualitative thresholds like mean and standard deviation of accuracy and computation speed.

Original languageEnglish
Pages (from-to)1135-1148
Number of pages14
JournalCluster Computing
Issue number2
Publication statusPublished - 2017 Jun 1

Bibliographical note

Publisher Copyright:
© 2017, The Author(s).

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications


Dive into the research topics of 'Optimized combinatorial clustering for stochastic processes'. Together they form a unique fingerprint.

Cite this