- Title
- A comparative study of unsupervised classification algorithms in multi-sized data sets
- Creator
- Quddus, Syed; Bagirov, Adil
- Date
- 2019
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/183034
- Identifier
- vital:16243
- Identifier
-
https://doi.org/10.1145/3375959.3375979
- Identifier
- ISBN:9781450372633 (ISBN)
- Abstract
- The ability to mine and extract useful information automatically, from large data sets, is a common concern for organizations, for the last few decades. Over the internet, data is vastly increasing gradually and consequently the capacity to collect and store very large data is significantly increasing. Existing clustering algorithms are not always efficient and accurate in solving clustering problems for large data sets. However, the development of accurate and fast data classification algorithms for very large scale data sets is still a challenge. In this paper, we present an overview of various algorithms and approaches which are recently being used for Clustering of large data and E-document. In this paper, a comparative study of the performance of various algorithms: the global kmeans algorithm (GKM), the multi-start modified global kmeans algorithm (MS-MGKM), the multi-start kmeans algorithm (MS-KM), the difference of convex clustering algorithm (DCA), the clustering algorithm based on the difference of convex representation of the cluster function and non-smooth optimization (DC-L2), is carried out using C++. © 2019 ACM.
- Publisher
- Association for Computing Machinery
- Relation
- 2nd Artificial Intelligence and Cloud Computing Conference, AICCC 2019, Kobe, 21-23 December 2019 p. 26-32
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2019 Association for Computing Machinery
- Subject
- Algorithms; Cluster Analysis; Data Mining
- Reviewed
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