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Overview of Different Methods Used in Clustering Algorithms

Yıl 2020, Cilt: 3 Sayı: 4, 173 - 189, 01.10.2020

Tohid Yousefi Mehmet Serhat Odabas Recai Oktaş

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.

Anahtar Kelimeler

Algorithm Clustering Partitional clustering Density-based clustering Hierarchical clustering

Kaynakça

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Kümeleme Algoritmalarında Kullanılan Farklı Yöntemlere Genel Bakış

Yıl 2020, Cilt: 3 Sayı: 4, 173 - 189, 01.10.2020

Tohid Yousefi Mehmet Serhat Odabas Recai Oktaş

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.

Anahtar Kelimeler

Algoritma Kümeleme Hiyerarşik kümeleme Bölümsel Kümeleme Yoğunluk tabanlı kümeleme

Kaynakça

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  • Anderberg MR. 2014.Cluster analysis for applications: probability and mathematical statistics: a series of monographs and textbooks, Cilt 19. Academic press.
  • Ankerst M, Breunig MM, Kriegel HP, Sander J. 1999. OPTICS: ordering points to identify the clustering structure. ACM SIGMOD Record, 28(2): 49-60.
  • 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.
  • Berggren JL. 2017.Episodes in the mathematics of medieval Islam: Springer.
  • Berkhin P. 2006. A survey of clustering data mining techniques. Grouping multidimensional data s. 25-71: Springer.
  • Bezdek JC. 1973. Cluster validity with fuzzy sets.
  • Bezdek JC. 1981. Objective function clustering. Pattern recognition with fuzzy objective function algorithms. 43-93: Springer.
  • Bezdek JC, Pal SK. 1994. Fuzzy models for pattern recognition.
  • Bezdek JC. 1980. A convergence theorem for the fuzzy ISODATA clustering algorithms. IEEE transact, 1: 1-8.
  • Bhat A. 2014. K-medoids clustering using partitioning around medoids for performing face recognition. Int J Soft Comput, Math Cont, 3(3): 1-12.
  • Bishop CM. 1995.Neural networks for pattern recognition: Oxford university press.
  • Blass A, Gurevich Y. 2004. Algorithms: A quest for absolute definitions. Current Trends in Theoretical Computer Science: The Challenge of the New Century Vol 1: Algorithms and Complexity Vol 2: Formal Models and Semantics, Scientific.
  • Böhm C, Noll R, Plant C, Wackersreuther B. 2009. Density-based clustering using graphics processors. Proceedings of the 18th ACM conference on Information and knowledge management.
  • Brecheisen S, Kriegel HP, Pfeifle M. 2004. Efficient density-based clustering of complex objects. Paper presented at the Fourth IEEE International Conference on Data Mining ICDM'04.
  • Brecheisen S, Kriegel HP, Pfeifle M. 2006. Parallel density-based clustering of complex objects. Pacific-Asia Conference on Knowledge Discovery and Data Mining.
  • Chakravarthy SV, Ghosh J. 1996. Scale-based clustering using the radial basis function network. IEEE Transact, 7(5): 1250-1261.
  • Chauhan R. 2014. Clustering Techniques: a comprehensive study of various clustering techniques. Inter J Advance Res Comput Sci, 5(5).
  • Chen S, Zhang D. 2004. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Transact, 34(4): 1907-1916.
  • 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.
  • Cord M,Cunningham P. 2008.Machine learning techniques for multimedia: case studies on organization and retrieval: Springer Science & Business Media.
  • Cormen TH, Leiserson CE, Rivest RL, Stein C. 2009.Introduction to algorithms: MIT press.
  • 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.
  • Deepa MS, Sujatha N. 2014. Comparative Studies of Various Clustering Techniques and Its Characteristics. Int J Adv Netw App, 5(6): 2104.
  • 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.
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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 Cilt: 3 Sayı: 4

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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