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Overview of Different Methods Used in Clustering Algorithms

Yıl 2020, , 173 - 189, 01.10.2020
https://doi.org/10.34248/bsengineering.698741

Öz

Data mining is the process of extracting meaningful information from large databases using many techniques and algorithms. Data mining is often referred to as "information discovery in data" and many methods are used to find this information. Clustering method, which is one of the basic methods of data mining, is one of the most powerful methods to analyze these data, while data is being produced rapidly in today's world. Clustering is the technique of finding natural groupings or clusters in data based on some similarity distances. Also clustering is essentially a fundamental step in many different data analyzes. Therefore, different methods used in clustering algorithms are briefly described in this review.

Kaynakça

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Kümeleme Algoritmalarında Kullanılan Farklı Yöntemlere Genel Bakış

Yıl 2020, , 173 - 189, 01.10.2020
https://doi.org/10.34248/bsengineering.698741

Öz

Veri madenciliği, birçok teknik ve algoritmayı kullanarak büyük veri tabanlarından anlamlı bilgileri çıkarma işlemidir. Veri madenciliği genellikle, “verilerde bilgi keşfi” olarak adlandırılan ve bu bilgileri bulmak için kullanılan yöntemlerdir. Veri madenciliğinin temel yöntemlerinden birisi olan kümeleme yöntemidir. Kümeleme yöntemi günümüz dünyasında hızla çoğalan verilerin analizinde kullanılacak en güçlü yöntemlerdendir. Kümeleme bazı benzerlik mesafelerine dayalı olarak verilerdeki doğal gruplamaları veya kümeleri bulma tekniğidir. Kümeleme aslında birçok farklı veri analizlerinde temel bir adımdır. Bundan dolayı bu derlemede kümeleme algoritmalarında kullanılan farklı yöntemler özet bir şekilde anlatılmıştır.

Kaynakça

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  • Belacel N, Wang Q,Cuperlovic-Culf M. 2006. Clustering methods for microarray gene expression data. OMICS, 104: 507-531.
  • Belli F, Beyazit M, Güler N. 2012. Event-Oriented, Model-Based GUI Testing and Reliability Assessment—Approach and Case Study. Advances in Computers, 85: 277-326.
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  • Cheng W, Wang W, Batista S. 2018. Grid-based clustering. Data Clustering s. 128-148: Chapman and Hall/CRC.
  • Cherng JS, Lo MJ. 2001. A hypergraph based clustering algorithm for spatial data sets. Proceedings 2001 IEEE International Conference on Data Mining.
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  • Cottrell M, Olteanu M, Rossi F, Villa Vialaneix N. 2018. Self-organizing maps, theory and applications.
  • Dabhi DP, Patel MR. 2016. Extensive survey on hierarchical clustering methods in data mining. Int Res J Eng and Tech IRJET, 3: 659-665.
  • Davies ER. 2004.Machine vision: theory, algorithms, practicalities: Elsevier.
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  • Dellaert F. 2002. The expectation maximization algorithm.
  • Dempster AP, Laird NM,Rubin DB. 1977. Maximum likelihood from incomplete data via the EM algorithm. J Royal Stat Soc: Series B Meth, 39(1): 1-22.
  • Dunham MH. 2006.Data mining: Introductory and advanced topics: Pearson Education India.
  • Edla DRJana PK. 2012. A grid clustering algorithm using cluster boundaries. World Congress on Information and Communication Technologies.
  • Emami H, Dami S, Shirazi H. 2015. K-Harmonic means data clustering with ımperialist competitive algorithm. UPB Sci Bull, Series C, 77(1): 91-104.
  • Erickson J. 2019.Algorithms Jeff Erickson.
  • Ester M, Kriegel HP, Sander J, Xu X. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. Paper presented at the Kdd.
  • Estivill Castro V. 2002. Why so many clustering algorithms: a position paper. ACM SIGKDD, 4(1): 65-75.
  • Everitt BS. 1979. Unresolved problems in cluster analysis. Biometrics, 169-181.
  • Firdaus S, Uddin MA. 2015. A survey on clustering algorithms and complexity analysis. Int J Comp Sci Iss IJCSI, 12(2): 62.
  • Fisher DH. 1987. Improving Inference through Conceptual Clustering. AAAI, 461-465.
  • Fisher DH. 1987. Knowledge acquisition via incremental conceptual clustering. Mach Learn, 2(2): 139-172.
  • Forgy EW. 1965. Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics, 21: 768-769.
  • Frigui H, Krishnapuram R. 1997. Clustering by competitive agglomeration. Pattern Recog, 30(7): 1109-1119.
  • Goldschlager L, Lister A. 1988. Computer science: a modern introduction: Prentice Hall International UK Ltd.
  • Goodrich MT, Tamassia R. 2014.Algorithm design and applications: Wiley Publishing.
  • Guha S, Rastogi R, Shim K. 1998. CURE: an efficient clustering algorithm for large databases. ACM Sigmod Rec, 27(2): 73-84.
  • Guha S, Rastogi R, Shim K. 2000. ROCK: A robust clustering algorithm for categorical attributes. Information Sys, 25(5): 345-366.
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Toplam 153 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Reviews
Yazarlar

Tohid Yousefi 0000-0003-4288-8194

Mehmet Serhat Odabas 0000-0002-1863-7566

Recai Oktaş 0000-0003-3282-3549

Yayımlanma Tarihi 1 Ekim 2020
Gönderilme Tarihi 4 Mart 2020
Kabul Tarihi 21 Nisan 2020
Yayımlandığı Sayı Yıl 2020

Kaynak Göster

APA Yousefi, T., Odabas, M. S., & Oktaş, R. (2020). Kümeleme Algoritmalarında Kullanılan Farklı Yöntemlere Genel Bakış. Black Sea Journal of Engineering and Science, 3(4), 173-189. https://doi.org/10.34248/bsengineering.698741
AMA Yousefi T, Odabas MS, Oktaş R. Kümeleme Algoritmalarında Kullanılan Farklı Yöntemlere Genel Bakış. BSJ Eng. Sci. Ekim 2020;3(4):173-189. doi:10.34248/bsengineering.698741
Chicago Yousefi, Tohid, Mehmet Serhat Odabas, ve Recai Oktaş. “Kümeleme Algoritmalarında Kullanılan Farklı Yöntemlere Genel Bakış”. Black Sea Journal of Engineering and Science 3, sy. 4 (Ekim 2020): 173-89. https://doi.org/10.34248/bsengineering.698741.
EndNote Yousefi T, Odabas MS, Oktaş R (01 Ekim 2020) Kümeleme Algoritmalarında Kullanılan Farklı Yöntemlere Genel Bakış. Black Sea Journal of Engineering and Science 3 4 173–189.
IEEE T. Yousefi, M. S. Odabas, ve R. Oktaş, “Kümeleme Algoritmalarında Kullanılan Farklı Yöntemlere Genel Bakış”, BSJ Eng. Sci., c. 3, sy. 4, ss. 173–189, 2020, doi: 10.34248/bsengineering.698741.
ISNAD Yousefi, Tohid vd. “Kümeleme Algoritmalarında Kullanılan Farklı Yöntemlere Genel Bakış”. Black Sea Journal of Engineering and Science 3/4 (Ekim 2020), 173-189. https://doi.org/10.34248/bsengineering.698741.
JAMA Yousefi T, Odabas MS, Oktaş R. Kümeleme Algoritmalarında Kullanılan Farklı Yöntemlere Genel Bakış. BSJ Eng. Sci. 2020;3:173–189.
MLA Yousefi, Tohid vd. “Kümeleme Algoritmalarında Kullanılan Farklı Yöntemlere Genel Bakış”. Black Sea Journal of Engineering and Science, c. 3, sy. 4, 2020, ss. 173-89, doi:10.34248/bsengineering.698741.
Vancouver Yousefi T, Odabas MS, Oktaş R. Kümeleme Algoritmalarında Kullanılan Farklı Yöntemlere Genel Bakış. BSJ Eng. Sci. 2020;3(4):173-89.

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