A Novel Hierarchical-Clustering-Combination Scheme Based on Fuzzy-Similarity Relations

Mirzaei, Abdolreza; Rahmati, Mohammad

doi:10.1109/tfuzz.2009.2034531

Cited by 70 publications

(30 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over the past years, several clustering ensemble techniques have been proposed in the literature [6][7][8][9][10][11] (see [1] for a survey). In general, very different mathematical and computational tools have been used for the development of clustering ensemble algorithms.…”

Section: Related Workmentioning

confidence: 99%

On pruning the search space for clustering ensemble problems

Vega-Pons

Avesani

2015

Neurocomputing

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

On pruning the search space for clustering ensemble problems

Vega-Pons

Avesani

2015

Neurocomputing

View full text Add to dashboard Cite

“…In the paper [5], the author addresses and outlined the hierarchical clustering combinations and introduces a novel algorithmic framework for combining the results into single entity. According to the suggested approach the similarity based data descriptive metrics of inputs are aggregated into transitive consensus matrix to for the final hierarchies.…”

Section: Literature Surveymentioning

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

“…Clustering process has a wide application area containing image processing, data mining, machine learning, and bioinformatics beside computer applications such as document or search engine listings. Due to clustering the large data repositories is required to provide meaningful data results to user, a variety of algorithm and method developing studies are performed to facilitate inferences [24][25][26][27][28][29]. Goldberger and Tassa [30] has proposed an algorithm based on Hungarian method, which is used to solve the label correspondence and averaging or different types of voting [24].…”

Section: The Substructure Of Prediction Tool Developedmentioning

confidence: 99%

“…Due to clustering the large data repositories is required to provide meaningful data results to user, a variety of algorithm and method developing studies are performed to facilitate inferences [24][25][26][27][28][29]. Goldberger and Tassa [30] has proposed an algorithm based on Hungarian method, which is used to solve the label correspondence and averaging or different types of voting [24]. Kusiak et al [31] have applied several approaches containing fuzzy set, evolutionary computation, principal component analysis, and residual approach to characterize power curves in a wind farm model.…”

Section: The Substructure Of Prediction Tool Developedmentioning

confidence: 99%

Development of a feasibility prediction tool for solar power plant installation analyses

Kabalcı

2011

Applied Energy

View full text Add to dashboard Cite

A Novel Hierarchical-Clustering-Combination Scheme Based on Fuzzy-Similarity Relations

Cited by 70 publications

References 52 publications

On pruning the search space for clustering ensemble problems

On pruning the search space for clustering ensemble problems

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

Development of a feasibility prediction tool for solar power plant installation analyses

Contact Info

Product

Resources

About