Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Annals of Data Science
Erschienen in: Annals of Data Science 2/2015

01.06.2015

A Comprehensive Survey of Clustering Algorithms

verfasst von: Dongkuan Xu, Yingjie Tian

Erschienen in: Annals of Data Science | Ausgabe 2/2015

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Data analysis is used as a common method in modern science research, which is across communication science, computer science and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along with the information increase and subject intersection. On the other hand, each clustering algorithm has its own strengths and weaknesses, due to the complexity of information. In this review paper, we begin at the definition of clustering, take the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyze the clustering algorithms from two perspectives, the traditional ones and the modern ones. All the discussed clustering algorithms will be compared in detail and comprehensively shown in Appendix Table 22.
Vorheriger Artikel Trinomial Response Modeling in One Logit Regression
Nächster Artikel Testing Exponentiality Based on the Likelihood Ratio and Power Comparison

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Anhänge
Nur mit Berechtigung zugänglich
Literatur
1.
Jain A, Dubes R (1988) Algorithms for clustering data. Prentice-Hall, Inc, Upper Saddle RiverMATH
2.
3.
Everitt B, Landau S, Leese M (2001) Clustering analysis, 4th edn. Arnold, London
4.
Gower J (1971) A general coefficient of similarity and some of its properties. Biometrics 27:857–871CrossRef
5.
Estivill-Castro V (2002) Why so many clustering algorithms: a position paper. ACM SIGKDD Explor Newsl 4:65–75CrossRef
6.
Färber I, Günnemann S, Kriegel H, Kröger P, Müller E, Schubert E, Seidl T, Zimek A (2010) On using class-labels in evaluation of clusterings. In MultiClust: 1st international workshop on discovering, summarizing and using multiple clusterings held in conjunction with KDD, Washington, DC
7.
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. Proc Fifth Berkeley Symp Math Stat Probab 1:281–297MathSciNet
8.
Park H, Jun C (2009) A simple and fast algorithm for K-medoids clustering. Expert Syst Appl 36:3336–3341CrossRef
9.
Kaufman L, Rousseeuw P (1990) Partitioning around medoids (program pam). Finding groups in data: an introduction to cluster analysis. Wiley, HobokenCrossRef
10.
11.
Ng R, Han J (2002) Clarans: a method for clustering objects for spatial data mining. IEEE Trans Knowl Data Eng 14:1003–1016CrossRef
12.
Boley D, Gini M, Gross R, Han E, Hastings K, Karypis G, Kumar V, Mobasher B, Moore J (1999) Partitioning-based clustering for web document categorization. Decis Support Syst 27:329–341CrossRef
13.
Jain A (2010) Data clustering: 50 years beyond K-means. Pattern Recognit Lett 31:651–666CrossRef
14.
Velmurugan T, Santhanam T (2011) A survey of partition based clustering algorithms in data mining: an experimental approach. Inf Technol J 10:478–484CrossRef
15.
16.
17.
Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. ACM SIGMOD Rec 25:103–104CrossRef
18.
Guha S, Rastogi R, Shim K (1998) CURE: an efficient clustering algorithm for large databases. ACM SIGMOD Rec 27:73–84CrossRef
19.
Guha S, Rastogi R, Shim K (1999) ROCK: a robust clustering algorithm for categorical attributes. In: Proceedings of the 15th international conference on data engineering, pp 512-521
20.
Karypis G, Han E, Kumar V (1999) Chameleon: hierarchical clustering using dynamic modeling. Computer 32:68–75CrossRef
21.
Murtagh F (1983) A survey of recent advances in hierarchical clustering algorithms. Comput J 26:354–359MATHCrossRef
22.
Carlsson G, Mémoli F (2010) Characterization, stability and convergence of hierarchical clustering methods. J Mach Learn Res 11:1425–1470MATHMathSciNet
23.
Dunn J (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3:32–57MATHMathSciNetCrossRef
24.
Bezdek J (1981) Pattern recognition with fuzzy objective function algorithms. Plenum, New YorkMATHCrossRef
25.
Bezdek J, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10:191–203ADSCrossRef
26.
Dave R, Bhaswan K (1992) Adaptive fuzzy c-shells clustering and detection of ellipses. IEEE Trans Neural Netw 3:643–662PubMedCrossRef
27.
Yager R, Filev D (1994) Approximate clustering via the mountain method. IEEE Trans Syst Man Cybern 24:1279–1284CrossRef
28.
29.
Baraldi A, Blonda P (1999) A survey of fuzzy clustering algorithms for pattern recognition. I. IEEE Trans Syst Man Cybern Part B 29:778–785CrossRef
30.
Höppner F (1999) Fuzzy cluster analysis: methods for classification, data analysis and image recognition. Wiley, HobokenMATH
31.
Xu X, Ester M, Kriegel H, Sander J (1998) A distribution-based clustering algorithm for mining in large spatial databases. In: Proceedings of the fourteenth international conference on data engineering, pp 324-331
32.
Rasmussen C (1999) The infinite Gaussian mixture model. Adv Neural Inf Process Syst 12:554–560
33.
Preheim S, Perrotta A, Martin-Platero A, Gupta A, Alm E (2013) Distribution-based clustering: using ecology to refine the operational taxonomic unit. Appl Environ Microbiol 79:6593–6603PubMedCentralPubMedCrossRef
34.
Jiang B, Pei J, Tao Y, Lin X (2013) Clustering uncertain data based on probability distribution similarity. IEEE Trans Knowl Data Eng 25:751–763CrossRef
35.
Kriegel H, Kröger P, Sander J, Zimek A (2011) Densitybased clustering. Wiley Interdiscip Rev 1:231–240
36.
Ester M, Kriegel H, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the second ACM SIGKDD international conference on knowledge discovery and data mining, pp 226–231
37.
Ankerst M, Breunig M, Kriegel H, Sander J (1999) OPTICS: ordering points to identify the clustering structure. In: Proceedings on 1999 ACM SIGMOD international conference on management of data, vol 28, pp 49–60
38.
Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24:603–619CrossRef
39.
Januzaj E, Kriegel H, Pfeifle M (2004) Scalable density-based distributed clustering. In: Proceedings of the 8th european conference on principles and practice of knowledge discovery in databases, pp 231–244
40.
Kriegel H, Pfeifle M (2005) Density-based clustering of uncertain data. In: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining, pp 672–677
41.
Chen Y, Tu L (2007) Density-based clustering for real-time stream data. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, pp 133–142
42.
Duan L, Xu L, Guo F, Lee J, Yan B (2007) A local-density based spatial clustering algorithm with noise. Inf Syst 32:978–986CrossRef
43.
Hinneburg A, Keim D (1998) An efficient approach to clustering in large multimedia databases with noise. In Proceedings of the 4th ACM SIGKDD international conference on knowledge discovery and data mining 98: 58–65
44.
Sharan R, Shamir R (2000) CLICK: a clustering algorithm with applications to gene expression analysis. In: Proc international conference intelligent systems molecular biolgy, pp 307–316
45.
Jain A, Murty M, Flynn P (1999) Data clustering: a review. ACM Comput Surv (CSUR) 31:264–323CrossRef
46.
Ben-Dor A, Shamir R, Yakhini Z (1999) Clustering gene expression patterns. J Comput Biol 6:281–297PubMedCrossRef
47.
48.
Estivill-Castro V, Lee I (2000) Amoeba: hierarchical clustering based on spatial proximity using delaunay diagram. In: Proceedings of the 9th international symposium on spatial data handling, Beijing
49.
Cherng J, Lo M (2001) A hypergraph based clustering algorithm for spatial data sets. In: Proceedings of the 2001 IEEE international conference on data mining, pp 83–90
50.
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22:888–905CrossRef
51.
Ng A, Jordan M, Weiss Y (2002) On spectral clustering: analysis and an algorithm. Adv Neural Inf Process Syst 2:849–856
52.
Wang W, Yang J, Muntz R (1997) STING: a statistical information grid approach to spatial data mining. In VLDB, pp 186–195
53.
Agrawal R, Gehrke J, Gunopulos D, Raghavan P (1998) Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings 1998 ACM sigmod international conference on management of data, vol 27, pp 94–105
54.
Sheikholeslami G, Chatterjee S, Zhang A (1998) Wavecluster: A multi-resolution clustering approach for very large spatial databases. In: VLDB, pp 428–439
55.
Ma E, Chow T (2004) A new shifting grid clustering algorithm. Pattern Recognit 37:503–514MATHCrossRef
56.
Park N, Lee W (2004) Statistical grid-based clustering over data streams. ACM SIGMOD Rec 33:32–37CrossRef
57.
Pilevar A, Sukumar M (2005) GCHL: a grid-clustering algorithm for high-dimensional very large spatial data bases. Pattern Recognit Lett 26:999–1010CrossRef
58.
Mandelbrot B (1983) The fractal geometry of nature. Macmillan, London
59.
Barbará D, Chen P (2000) Using the fractal dimension to cluster datasets. In: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, pp 260–264
60.
Zhang A, Cheng B, Acharya R (1996) A fractal-based clustering approach in large visual database systems. In Representation and retrieval of visual media in, multimedia systems, pp 49–68
61.
Menascé D, Abrahao B, Barbará D, Almeida V, Ribeiro F (2002) Fractal characterization of web workloads. In: Proceedings of the “ Web Engineering” Track of WWW2002, pp 7–11
62.
Barry R, Kinsner W (2004) Multifractal characterization for classification of network traffic. Conf Electr Comput Eng 3:1453–1457
63.
Al-Shammary D, Khalil I, Tari Z (2014) A distributed aggregation and fast fractal clustering approach for SOAP traffic. J Netw Comput Appl 41:1–14CrossRef
64.
Fisher D (1987) Knowledge acquisition via incremental conceptual clustering. Mach Learn 2:139–172ADS
65.
KohonenKohonen T (1990) The self-organizing map. Proc IEEE 78:1464–1480CrossRef
66.
Carpenter G, Grossberg S (1987) A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput Vis Gr Image Process 37:54–115MATHCrossRef
67.
Carpenter G, Grossberg S (1988) The ART of adaptive pattern recognition by a self-organizing neural network. Computer 21:77–88CrossRef
68.
Carpenter G, Grossberg S (1987) ART 2: self-organization of stable category recognition codes for analog input patterns. Appl Opt 26:4919–4930PubMedADSCrossRef
69.
Carpenter G, Grossberg S (1990) ART 3: hierarchical search using chemical transmitters in self-organizing pattern recognition architectures. Neural Netw 3:129–152CrossRef
70.
Meilă M, Heckerman D (2001) An experimental comparison of model-based clustering methods. Mach Learn 42:9–29MATHCrossRef
71.
Fraley C, Raftery A (2002) Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc 97:611–631MATHMathSciNetCrossRef
72.
McLachlan G, Bean R, Peel D (2002) A mixture model-based approach to the clustering of microarray expression data. Bioinformatics 18:413–422PubMedCrossRef
73.
Medvedovic M, Sivaganesan S (2002) Bayesian infinite mixture model based clustering of gene expression profiles. Bioinformatics 18:1194–1206PubMedCrossRef
74.
Zhong S, Ghosh J (2003) A unified framework for model-based clustering. J Mach Learn Res 4:1001–1037MathSciNet
75.
McNicholas P, Murphy T (2010) Model-based clustering of microarray expression data via latent Gaussian mixture models. Bioinformatics 26:2705–2712PubMedCrossRef
76.
Schölkopf B, Smola A, Müller K (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10:1299–1319CrossRef
77.
MacDonald D, Fyfe C (2000) The kernel self-organising map. Proc Fourth Int Conf Knowl-Based Intell Eng Syst Allied Technol 1:317–320
78.
Wu Z, Xie W,Yu J (2003) Fuzzy c-means clustering algorithm based on kernel method. In: Proceedings of the fifth ICCIMA, pp 49–54
79.
Ben-Hur A, Horn D, Siegelmann H, Vapnik V (2002) Support vector clustering. J Mach Learn Res 2:125–137MATH
80.
Xu L, Neufeld J, Larson B, Schuurmans D (2004) Maximum margin clustering. In: Advances in neural information processing systems, pp 1537–1544
81.
Zhao B, Kwok J, Zhang C (2009) Multiple kernel clustering. In SDM, pp 638–649
82.
Müller K, Mika S, Rätsch G, Tsuda K, Schölkopf B (2001) An introduction to kernel-based learning algorithms. IEEE Trans Neural Netw 12:181–201PubMedCrossRef
83.
Girolami M (2002) Mercer kernel-based clustering in feature space. IEEE Trans Neural Netw 13:780–784PubMedCrossRef
84.
Filippone M, Camastra F, Masulli F, Rovetta S (2008) A survey of kernel and spectral methods for clustering. Pattern Recognit 41:176–190MATHCrossRef
85.
Fred A, Jain A (2005) Combining multiple clusterings using evidence accumulation. IEEE Trans Pattern Anal Mach Intell 27:835–850PubMedCrossRef
86.
Strehl A, Ghosh J (2003) Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3:583–617MATHMathSciNet
87.
Fern X, Brodley C (2003) Random projection for high dimensional data clustering: a cluster ensemble approach. ICML 3:186–193
88.
Dimitriadou E, Weingessel A, Hornik K (2001) Voting-merging: an ensemble method for clustering. In: ICANN, pp 217–224
89.
Topchy A, Jain A, Punch W (2004) A mixture model for clustering ensembles. In: Proceedings of the SIAM international conference on data mining, pp 379
90.
Topchy A, Jain A, Punch W (2005) Clustering ensembles: models of consensus and weak partitions. IEEE Trans Pattern Anal Mach Intell 27:1866–1881PubMedCrossRef
91.
Yoon H, Ahn S, Lee S, Cho S, Kim J (2006) Heterogeneous clustering ensemble method for combining different cluster results. In: Data mining for biomedical applications, pp 82–92
92.
Domeniconi C, Gunopulos D, Ma S, Yan B, Al-Razgan M, Papadopoulos D (2007) Locally adaptive metrics for clustering high dimensional data. Data Min Knowl Discov 14:63–97MathSciNetCrossRef
93.
Vega-Pons S, Correa-Morris J, Ruiz-Shulcloper J (2010) Weighted partition consensus via kernels. Pattern Recognit 43:2712–2724MATHCrossRef
94.
Punera K, Ghosh J (2008) Consensus-based ensembles of soft clusterings. Appl Artif Intell 22:780–810CrossRef
95.
Vega-Pons S, Ruiz-Shulcloper J (2011) A survey of clustering ensemble algorithms. Int J Pattern Recognit Artif Intell 25:337–372MathSciNetCrossRef
96.
Handl J, Meyer B (2007) Ant-based and swarm-based clustering. Swarm Intell 1:95–113CrossRef
97.
Abraham A, Das S, Roy S (2008) Swarm intelligence algorithms for data clustering. In: Soft computing for knowledge discovery and data mining, pp 279–313
98.
Van der Merwe D, Engelbrecht A (2003) Data clustering using particle swarm optimization. Congr Evol Comput 1:215–220
99.
Amiri B, Fathian M, Maroosi A (2009) Application of shuffled frog-leaping algorithm on clustering. Int J Adv Manuf Technol 45:199–209CrossRef
100.
Karaboga D, Ozturk C (2011) A novel clustering approach: artificial bee colony (ABC) algorithm. Appl Soft Comput 11:652–657CrossRef
101.
Lumer E, Faieta B (1994) Diversity and adaptation in populations of clustering ants. Proc Third Int Conf Simul Adapt Behav 3:501–508
102.
Shelokar P, Jayaraman V, Kulkarni B (2004) An ant colony approach for clustering. Anal Chim Acta 509:187–195CrossRef
103.
Karaboga D, Akay B (2009) A survey: algorithms simulating bee swarm intelligence. Artif Intell Rev 31:61–85CrossRef
104.
Xu R, Xu J, Wunsch D (2012) A comparison study of validity indices on swarm-intelligence-based clustering. IEEE Trans Syst Man Cybern Part B 42:1243–1256
105.
Horn D, Gottlieb A (2001) Algorithm for data clustering in pattern recognition problems based on quantum mechanics. Phys Rev Lett 88:018702PubMedADSCrossRef
106.
Horn D, Gottlieb A (2001) The method of quantum clustering. In: Advances in neural information processing systems, pp 769–776
107.
Weinstein M, Horn D (2009) Dynamic quantum clustering: a method for visual exploration of structures in data. Phys Rev E 80:066117ADSCrossRef
108.
109.
Horn D, Axel I (2003) Novel clustering algorithm for microarray expression data in a truncated SVD space. Bioinformatics 19:1110–1115PubMedCrossRef
110.
Aïmeur E, Brassard G, Gambs S (2007) Quantum clustering algorithms. In: ICML, pp 1–8
111.
112.
Yu S, Shi J (2003) Multiclass spectral clustering. In: Proceedings of the ninth IEEE international conference on computer vision, pp 313–319
113.
Verma D, Meila M (2003) A comparison of spectral clustering algorithms. University of Washington Tech Rep UWCSE030501 1: 1–18
114.
Chen W, Song Y, Bai H, Lin C, Chang E (2011) Parallel spectral clustering in distributed systems. IEEE Trans Pattern Anal Mach Intell 33:568–586PubMedCrossRef
115.
Lu Z, Carreira-Perpinan M (2008) Constrained spectral clustering through affinity propagation. In: IEEE conference on computer vision and pattern recognition, pp 1–8
116.
117.
Shang F, Jiao L, Shi J, Wang F, Gong M (2012) Fast affinity propagation clustering: a multilevel approach. Pattern Recognit 45:474–486CrossRef
118.
119.
Ng R, Han J (1994) Efficient and effective clustering methods for spatial data mining. In: VLDB, pp 144–155
120.
Sander J, Ester M, Kriegel H, Xu X (1998) Density-based clustering in spatial databases: the algorithm gdbscan and its applications. Data Min Knowl Discov 2:169–194CrossRef
121.
Harel D, Koren Y (2001) Clustering spatial data using random walks. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, pp 281–286
122.
Zaïane O, Lee C (2002) Clustering spatial data when facing physical constraints. In: Proceedings of the IEEE international conference on data mining, pp 737–740
123.
Birant D, Kut A (2007) ST-DBSCAN: an algorithm for clustering spatial-temporal data. Data Knowl Eng 60:208–221CrossRef
124.
O’callaghan L, Meyerson A, Motwani R, Mishra N, Guha S (2002) Streaming-data algorithms for high-quality clustering. In: ICDE, p 0685
125.
Aggarwal C, Han J, Wang J, Yu P (2003) A framework for clustering evolving data streams. In: VLDB, pp 81–92
126.
Aggarwal C, Han J, Wang J, Yu P (2004) A framework for projected clustering of high dimensional data streams. In: VLDB, pp 852–863
127.
Cao F, Ester M, Qian W, Zhou A (2006) Density-based clustering over an evolving data stream with noise. SDM 6:328–339MathSciNet
128.
Guha S, Mishra N, Motwani R, O’Callaghan L (2000) Clustering data streams. In: Proceedings of the 41st annual symposium on foundations of computer science, pp 359–366
129.
Barbará D (2002) Requirements for clustering data streams. ACM SIGKDD Explor Newsl 3:23–27CrossRef
130.
Guha S, Meyerson A, Mishra N, Motwani R, O’Callaghan L (2003) Clustering data streams: theory and practice. IEEE Trans Knowl Data Eng 15:515–528CrossRef
131.
Beringer J, Hüllermeier E (2006) Online clustering of parallel data streams. Data Knowl Eng 58:180–204CrossRef
132.
Silva J, Faria E, Barros R, Hruschka E, de Carvalho A, Gama J (2013) Data stream clustering: a survey. ACM Comput Surv 46:13CrossRef
133.
Leskovec J, Rajaraman A, Ullman JD (2014) Mining massive datasets. Cambridge University Press, CambridgeCrossRef
134.
Steinbach M, Karypis G, Kumar V (2000) A comparison of document clustering techniques. KDD Workshop Text Min 400:525–526
135.
Parsons L, Haque E, Liu H (2004) Subspace clustering for high dimensional data: a review. ACM SIGKDD Explor Newsl 6:90–105CrossRef
136.
Kriegel H, Kröger P, Zimek A (2009) Clustering high-dimensional data: a survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Trans Knowl Discov Data 3:1CrossRef
137.
Judd D, McKinley P, Jain A (1996) Large-scale parallel data clustering. In: Proceedings of the 13th international conference on pattern recognition, vol 4, pp 488–493
138.
Tasoulis D, Vrahatis M (2004) Unsupervised distributed clustering. In: Parallel and distributed computing and networks, pp 347–351
139.
Zhao W, Ma H, He Q (2009) Parallel k-means clustering based on mapreduce. In: Cloud computing, pp 674–679
140.
Herwig R, Poustka A, Müller C, Bull C, Lehrach H, O’Brien J (1999) Large-scale clustering of cDNA-fingerprinting data. Genome Res 9:1093–1105PubMedCentralPubMedCrossRef
141.
Hinneburg A, Keim D (2003) A general approach to clustering in large databases with noise. Knowl Inf Syst 5:387–415CrossRef
Metadaten
Titel
A Comprehensive Survey of Clustering Algorithms
verfasst von
Dongkuan Xu
Yingjie Tian
Publikationsdatum
01.06.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Annals of Data Science / Ausgabe 2/2015
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-015-0040-1

Weitere Artikel der Ausgabe 2/2015

Annals of Data Science 2/2015 Zur Ausgabe

OriginalPaper

Testing Exponentiality Based on the Likelihood Ratio and Power Comparison

OriginalPaper

Identifying High-Number-Cluster Structures in RFID Ski Lift Gates Entrance Data

OriginalPaper

Multilevel Modeling of the Progression of HIV/AIDS Disease Among Patients Under HAART Treatment

ReviewPaper

ELES-Model Based Housing Affordability Comparative Research of Urban Households in Beijing Between 2004 and 2014

OriginalPaper

Entropy Estimation Using Numerical Methods

OriginalPaper

Trinomial Response Modeling in One Logit Regression

Premium Partner

    Bildnachweise