A Multi-clustering Fusion Algorithm

Frossyniotis, Dimitrios S.; Pertselakis, Minas; Stafylopatis, Andreas

doi:10.1007/3-540-46014-4_21

Cited by 28 publications

(30 citation statements)

References 6 publications

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“…Cluster ensemble techniques aims to improve the clustering scheme by intelligently combining multiple schemes. This technique has caught attention of researchers in computer science community as it has found to substantially improve the robustness, stability, accuracy and quality of resulting clustering scheme [9][10][11][12][13]. An informative survey of various cluster ensemble techniques can be found in [9].…”

Section: Cluster Ensemble Approachmentioning

confidence: 99%

Regionalization of Rainfall Using RCDA Cluster Ensemble Algorithm in India

Ahuja¹,

Dhanya

2012

JSEA

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The magnitude and frequency of precipitation is of great significance in the field of hydrologic and hydraulic design and has wide applications in varied areas. However, the availability of precipitation data is limited to a few areas, where the rain gauges are successfully and efficiently installed. The magnitude and frequency of precipitation in ungauged sites can be assessed by grouping areas with similar characteristics. The procedure of grouping of areas having similar behaviour is termed as Regionalization. In this paper, RCDA cluster ensemble algorithm is employed to identify the homogeneous regions of rainfall in India. Cluster ensemble methods are commonly used to enhance the quality of clustering by combining multiple clustering schemes to produce a more robust scheme delivering similar homogeneous regions. The goal is to identify, analyse and describe hydrologically similar regions using RCDA cluster ensemble algorithm. RCDA cluster ensemble algorithm, which is based on discriminant analysis. The algorithm takes H base clustering schemes each with K clusters, obtained by any clustering method, as input and constructs discriminant function for each one of them. Subsequently, all the data tuples are predicted using H discriminant functions for cluster membership. Tuples with consistent predictions are assigned to the clusters, while tuples with inconsistent predictions are analyzed further and either assigned to clusters or declared as noise. RCDA algorithm has been compared with Best of K-means and Clue cluster ensemble of R software using traditional clustering quality measures. Further, domain knowledge based comparison has also been performed. All the results are encouraging and indicate better regionalization of the rainfall in different parts of India.

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Section: Cluster Ensemble Approachmentioning

confidence: 99%

Regionalization of Rainfall Using RCDA Cluster Ensemble Algorithm in India

Ahuja¹,

Dhanya

2012

JSEA

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“…Clustering hyperedges is performed by using graph-partitioning algorithms. The same core idea can also be found in [10,[14][15][16]. In [10], different clustering solutions are obtained by resampling and are aligned with the clusters estimated on all the data.…”

Section: Related Workmentioning

confidence: 99%

“…In [10], different clustering solutions are obtained by resampling and are aligned with the clusters estimated on all the data. In both [14,15], the different clustering solutions are obtained by multiple runs of the k-means algorithm with different initial conditions. An agglomerative pairwise cluster merging scheme is used, with a heuristic to determine the corresponding clusters.…”

Section: Related Workmentioning

confidence: 99%

Combining Multiple Clustering Systems

Boulis

Ostendorf

2004

Lecture Notes in Computer Science

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Abstract. Three methods for combining multiple clustering systems are presented and evaluated, focusing on the problem of finding the correspondence between clusters of different systems. In this work, the clusters of individual systems are represented in a common space and their correspondence estimated by either "clustering clusters" or with Singular Value Decomposition. The approaches are evaluated for the task of topic discovery on three major corpora and eight different clustering algorithms and it is shown experimentally that combination schemes almost always offer gains compared to single systems, but gains from using a combination scheme depend on the underlying clustering systems.

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“…Among these names we can quote: consensus clustering, clustering aggregation, clustering combination, fusion of clustering, ...etc. Several studies (Frossyniotis et al, 2002;Minaei-Bidgoli et al, 2004;Strehl & Ghosh, 2002;Topchy et al, 2004; have pioneered clustering data sets as a new branch of the conventional clustering methodology. In ) the authors propose a probabilistic formalism of clustering concensus using a finite mixture of multinomial distributions in a space of clustering.…”

Section: Introductionmentioning

confidence: 99%

“…In ) the authors propose a probabilistic formalism of clustering concensus using a finite mixture of multinomial distributions in a space of clustering. The approach proposed in (Frossyniotis et al, 2002) is designed for combining runs of clustering algorithms with the same number of clusters. In (Strehl & Ghosh, 2002) the authors proposed combiners based on a hyper-graph model to solve the cluster fusion problem.…”

Section: Introductionmentioning

confidence: 99%