Comparative study of matrix refinement approaches for ensemble clustering
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 605478066 | ||
| 003 | CHVBK | ||
| 005 | 20210128100404.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150101xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10994-013-5342-y |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-013-5342-y | ||
| 245 | 0 | 0 | |a Comparative study of matrix refinement approaches for ensemble clustering |h [Elektronische Daten] |c [Natthakan Iam-On, Tossapon Boongoen] |
| 520 | 3 | |a Cluster ensembles or consensus clusterings have been shown to be better than any standard clustering algorithm at improving accuracy and robustness across various sets of data. This meta-learning formalism also helps users to overcome the dilemma of selecting an appropriate technique and the parameters for that technique. Since founded, different research areas have emerged with the common purpose of enhancing the effectiveness and applicability of cluster ensembles. These include the selection of ensemble members, the imputation of missing values, and the summarization of ensemble members. In particular, this paper is set to provide the review of different matrix refinement approaches that have been recently proposed in the literature for summarizing information of multiple clusterings. With various benchmark datasets and quality measures, the comparative study of these novel techniques is carried out to provide empirical findings from which a practical guideline can be drawn. | |
| 540 | |a The Author(s), 2013 | ||
| 690 | 7 | |a Cluster ensemble |2 nationallicence | |
| 690 | 7 | |a Multiple clusterings |2 nationallicence | |
| 690 | 7 | |a Summarization |2 nationallicence | |
| 690 | 7 | |a Information matrix |2 nationallicence | |
| 700 | 1 | |a Iam-On |D Natthakan |u School of Information Technology, Mae Fah Luang University, 57100, Chiang Rai, Thailand |4 aut | |
| 700 | 1 | |a Boongoen |D Tossapon |u Department of Mathematics and Computer Science, Royal Thai Air Force Academy, 10220, Bangkok, Thailand |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 98/1-2(2015-01-01), 269-300 |x 0885-6125 |q 98:1-2<269 |1 2015 |2 98 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-013-5342-y |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-013-5342-y |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Iam-On |D Natthakan |u School of Information Technology, Mae Fah Luang University, 57100, Chiang Rai, Thailand |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Boongoen |D Tossapon |u Department of Mathematics and Computer Science, Royal Thai Air Force Academy, 10220, Bangkok, Thailand |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 98/1-2(2015-01-01), 269-300 |x 0885-6125 |q 98:1-2<269 |1 2015 |2 98 |o 10994 | ||