H.6.4. Clustering
Entropy-based Consensus for Distributed Data Clustering

M. Owhadi-Kareshki; M.R. Akbarzadeh-T.

Volume 7, Issue 4 , November 2019, , Pages 551-561

http://dx.doi.org/10.22044/jadm.2018.4237.1514

Abstract
  The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality ...  Read More

H.6.4. Clustering
Grouping Objects to Homogeneous Classes Satisfying Requisite Mass

M. Manteqipour; A.R. Ghaffari Hadigheh; R. Mahmoodvand; A. Safari

Volume 6, Issue 1 , March 2018, , Pages 163-175

http://dx.doi.org/10.22044/jadm.2017.988

Abstract
  Grouping datasets plays an important role in many scientific researches. Depending on data features and applications, different constrains are imposed on groups, while having groups with similar members is always a main criterion. In this paper, we propose an algorithm for grouping the objects with random ...  Read More

H.6.4. Clustering
Improved COA with Chaotic Initialization and Intelligent Migration for Data Clustering

M. Lashkari; M. Moattar

Volume 5, Issue 2 , July 2017, , Pages 293-305

http://dx.doi.org/10.22044/jadm.2016.783

Abstract
  A well-known clustering algorithm is K-means. This algorithm, besides advantages such as high speed and ease of employment, suffers from the problem of local optima. In order to overcome this problem, a lot of studies have been done in clustering. This paper presents a hybrid Extended Cuckoo Optimization ...  Read More

H.6.4. Clustering
A Multi-Objective Approach to Fuzzy Clustering using ITLBO Algorithm

P. Shahsamandi Esfahani; A. Saghaei

Volume 5, Issue 2 , July 2017, , Pages 307-317

http://dx.doi.org/10.22044/jadm.2016.784

Abstract
  Data clustering is one of the most important areas of research in data mining and knowledge discovery. Recent research in this area has shown that the best clustering results can be achieved using multi-objective methods. In other words, assuming more than one criterion as objective functions for clustering ...  Read More