Experimental Comparison of Cluster Ensemble Methods

Kuncheva, Ludmila I.; Hadjitodorov, Stefan; Todorova, Lyudmila

doi:10.1109/icif.2006.301614

Cited by 63 publications

(39 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In these applications, the outputs provided by different techniques are combined by one of several strategies in order to provide a consensus output value. The main goal is the improvement of the overall performance in terms of accuracy or precision by trying to use the best features of each individual technique [86]. For such, these approaches use either the label of the class (classification) or the desired value (regression).…”

Section: B Ensemble-based Evolutionary Clusteringmentioning

confidence: 99%

A Survey of Evolutionary Algorithms for Clustering

Hruschka

Campello

Freitas

et al. 2009

IEEE Trans. Syst., Man, Cybern. C

639

316

View full text Add to dashboard Cite

-This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context, most of the paper is devoted to partitional algorithms that look for hard clusterings of data, though overlapping (i.e., soft and fuzzy) approaches are also covered in the manuscript. The paper is original in what concerns two main aspects. First, it provides an up-to-date overview that is fully devoted to evolutionary algorithms for clustering, is not limited to any particular kind of evolutionary approach, and comprises advanced topics, like multi-objective and ensemble-based evolutionary clustering. Second, it provides a taxonomy that highlights some very important aspects in the context of evolutionary data clustering, namely, fixed or variable number of clusters, cluster-oriented or non-oriented operators, context-sensitive or context-insensitive operators, guided or unguided operators, binary, integer or real encodings, centroid-based, medoid-based, label-based, tree-based or graph-based representations, among others. A number of references is provided that describe applications of evolutionary algorithms for clustering in different domains, such as image processing, computer security, and bioinformatics. The paper ends by addressing some important issues and open questions that can be subject of future research.

show abstract

Section: B Ensemble-based Evolutionary Clusteringmentioning

confidence: 99%

A Survey of Evolutionary Algorithms for Clustering

Hruschka

Campello

Freitas

et al. 2009

IEEE Trans. Syst., Man, Cybern. C

639

316

View full text Add to dashboard Cite

show abstract

“…As for the clustering component, we built a similarity matrix from five data partitions induced by using K-Medoids clustering algorithm [51] with cosine similarity. Based on Kuncheva et al [52], [53], each data partition assumed a specific value for the number of clusters k = {2,3,5,6,8}. These values were randomly selected and fixed for all datasets.…”

Section: Svm and Cluster Ensemble Settingsmentioning

confidence: 99%

Combining Classification and Clustering for Tweet Sentiment Analysis

Coletta

Silva

Hruschka

et al. 2014

2014 Brazilian Conference on Intelligent Systems

View full text Add to dashboard Cite

Abstract-The goal of sentiment analysis is to determine opinions, emotions, and attitudes presented in source material. In tweet sentiment analysis, opinions in messages can be typically categorized as positive or negative. To classify them, researchers have been using traditional classifiers like Naive Bayes, Maximum Entropy, and Support Vector Machines (SVM). In this paper, we show that a SVM classifier combined with a cluster ensemble can offer better classification accuracies than a stand-alone SVM. In our study, we employed an algorithm, named C 3 E-SL, capable to combine classifier and cluster ensembles. This algorithm can refine tweet classifications from additional information provided by clusterers, assuming that similar instances from the same clusters are more likely to share the same class label. The resulting classifier has shown to be competitive with the best results found so far in the literature, thereby suggesting that the studied approach is promising for tweet sentiment classification.

show abstract

“…The above methods are also uses various internal cluster validation indices used for ensemble formation with hard generation consensus functions. [11].…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Novel Ensemble based Cluster Analysis using Similarity Matrices and Clustering Algorithm (SMCA)

Gupta¹,

Varma²

2014

IJCA

View full text Add to dashboard Cite

In today's world data analytics is gaining popularity due to user's motivation towards online data storage. This storage is not organized because of content types and data handling schemes complexity. User aims to retrieve data in lesser time with logical outcomes as desired can be achieved by applying data mining. Clustering in data mining is one of the known categorization approach used for formation of groups of similar elements having certain properties in common with other elements. This formation sometime creates noisy result in terms of formatted clusters. It depends on various factors such as distance measures, proximity values, objective functions, categorical or numerical attribute types etc. Over the last few years various schemes are suggested by different authors for improving the performance of tradition clustering algorithms. Among them, one is ensemble based clustering. Ensemble uses the mechanism for criteria selection from newly formed clusters with a defined portioning and joining methods to generate a single result instead of multiple solutions. The generation results are affected by various environmental parameters such as number of cluster, partitioning types, proximity values, objective function etc. This paper propose a novel SMCA based ensemble clustering algorithm for improvements over the existing issues defined in the paper. At the primary level of work and analytical evaluations, it shows the promising results in near future.

show abstract

Experimental Comparison of Cluster Ensemble Methods

Cited by 63 publications

References 13 publications

A Survey of Evolutionary Algorithms for Clustering

A Survey of Evolutionary Algorithms for Clustering

Combining Classification and Clustering for Tweet Sentiment Analysis

A Novel Ensemble based Cluster Analysis using Similarity Matrices and Clustering Algorithm (SMCA)

Contact Info

Product

Resources

About