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An incremental fuzzy algorithm for data clustering problems

Cilt: 21 Sayı: 1 15 Mart 2019
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An incremental fuzzy algorithm for data clustering problems

Öz

Data Cluster analysis is an important part of data mining. It can be handled as two types, hard and soft clustering. In hard clustering, a dataset is divided into distinct clusters and each data in the dataset belongs to exactly one cluster. On the contrary data can belong to more than one cluster in soft clustering and each data can be associated with each cluster by a membership degree. Incremental algorithms which are developed for hard clustering have two main advantages. They based on the nonsmooth-nonconvex mathematical model which allows significantly reduce the number of variables and they choose one cluster center for each step that leads to obtain better objective function. In this paper, we propose an incremental fuzzy algorithm for soft clustering problems and present results of numerical experiments on 11 real-world datasets. These results demonstrate that the proposed algorithm is efficient for solving the soft clustering problems.

Anahtar Kelimeler

Kaynakça

  1. Zadeh, A.L., Fuzzy sets, Information and Control, 8, 338-353, (1965).
  2. Al-Sultan, K.S., A tabu search approach to the clustering problem, Pattern Recognition, 28(9), 1443-1451, (1995).
  3. Brown, D.E., Entail, C.L., A practical application of simulated annealing to the clustering problem, Pattern Recognition, 25, 401-412, (1992).
  4. Diehr, G., Evaluation of a branch and bound algorithm for clustering, SIAM J. Scientific and Statistical Computing, 6, 268-284, (1985).
  5. Dubes, R., Jain, A.K., Clustering techniques: the user's dilemma, Pattern Recognition, 8, 247-260, (1976).
  6. Hanjoul, P., Peeters, D., A comparison of two dual-based procedures for solving the p-median problem, European Journal of Operational Research, 20, 387-396, (1985).
  7. Hansen, P., Jaumard, B., Cluster analysis and mathematical programming, Mathematical Programming, 79(1-3), 191-215, (1997).
  8. Hansen, P., Mladenovic, N., J-means: a new heuristic for minimum sum-of-squares clustering, Pattern Recognition, 4, 405-413, (2001).

Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

15 Mart 2019

Gönderilme Tarihi

28 Mart 2018

Kabul Tarihi

18 Ağustos 2018

Yayımlandığı Sayı

Yıl 2019 Cilt: 21 Sayı: 1

Kaynak Göster

APA
Nasibov, E., & Ordin, B. (2019). An incremental fuzzy algorithm for data clustering problems. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 21(1), 169-183. https://doi.org/10.25092/baunfbed.532619
AMA
1.Nasibov E, Ordin B. An incremental fuzzy algorithm for data clustering problems. BAUN Fen. Bil. Enst. Dergisi. 2019;21(1):169-183. doi:10.25092/baunfbed.532619
Chicago
Nasibov, Elvin, ve Burak Ordin. 2019. “An incremental fuzzy algorithm for data clustering problems”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 21 (1): 169-83. https://doi.org/10.25092/baunfbed.532619.
EndNote
Nasibov E, Ordin B (01 Mart 2019) An incremental fuzzy algorithm for data clustering problems. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 21 1 169–183.
IEEE
[1]E. Nasibov ve B. Ordin, “An incremental fuzzy algorithm for data clustering problems”, BAUN Fen. Bil. Enst. Dergisi, c. 21, sy 1, ss. 169–183, Mar. 2019, doi: 10.25092/baunfbed.532619.
ISNAD
Nasibov, Elvin - Ordin, Burak. “An incremental fuzzy algorithm for data clustering problems”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 21/1 (01 Mart 2019): 169-183. https://doi.org/10.25092/baunfbed.532619.
JAMA
1.Nasibov E, Ordin B. An incremental fuzzy algorithm for data clustering problems. BAUN Fen. Bil. Enst. Dergisi. 2019;21:169–183.
MLA
Nasibov, Elvin, ve Burak Ordin. “An incremental fuzzy algorithm for data clustering problems”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 21, sy 1, Mart 2019, ss. 169-83, doi:10.25092/baunfbed.532619.
Vancouver
1.Elvin Nasibov, Burak Ordin. An incremental fuzzy algorithm for data clustering problems. BAUN Fen. Bil. Enst. Dergisi. 01 Mart 2019;21(1):169-83. doi:10.25092/baunfbed.532619