New Efficient Strategy to Accelerate k-Means Clustering Algorithm

Zoubi, Moh’d Belal Al; Hudaib, Amjad; Huneiti, Ammar; Hammo, Bassam

doi:10.3844/ajassp.2008.1247.1250

Cited by 23 publications

(18 citation statements)

References 16 publications

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“…Data clustering techniques are an important aspect used in many fields such as data mining [20], pattern recognition and pattern classification [3], data compression, machine learning [8], image analysis [26], and bioinformatics [22]. The purpose of clustering is to group data points into clusters in which the similar data points are grouped in the same cluster while dissimilar data points are in different clusters.…”

Section: Data Clusteringmentioning

confidence: 99%

Clustering Algorithms in Echo State Networks

Ashour

Abu-Issa

Hellwich

2013

IJSIP

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In this work, we develop a new method of setting the input to reservoir and reservoir to reservoir weights in echo state machines. We use a clustering technique which we have previously developed as a pre-processing stage to set the reservoir parameters which at this stage are prototypes. We then use these prototypes as weights in the standard architecture while setting the reservoir to output weights in a standard manner. We show results on a variety of data sets in the literature which show that this method out-performs a standard random echo state machine.

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Section: Data Clusteringmentioning

confidence: 99%

Clustering Algorithms in Echo State Networks

Ashour

Abu-Issa

Hellwich

2013

IJSIP

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“…While K-Means discovers hard clusters (a point belong to only one cluster), Fuzzy K-Means is a more statistically formalized method and discovers soft clusters where a particular point can belong to more than one cluster with certain probability. In this study, we propose a new strategy to accelerate the k-means clustering algorithm (Al-Zoubi et al, 2008) through the Partial Distance (PD) logic. The Fuzzy K-means accepts an input file containing vector points student and faculty data sets.…”

Section: Methodsmentioning

confidence: 99%

Fuzzy Modeled K-Cluster Quality Mining of Hidden Knowledge for Decision Support

Kumar¹

2011

Journal of Computer Science

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Problem statement:The work presented Fuzzy Modeled K-means Cluster Quality Mining of hidden knowledge for Decision Support. Based on the number of clusters, number of objects in each cluster and its cohesiveness, precision and recall values, the cluster quality metrics is measured. The fuzzy k-means is adapted approach by using heuristic method which iterates the cluster to form an efficient valid cluster. With the obtained data clusters, quality assessment is made by predictive mining using decision tree model. Validation criteria focus on the quality metrics of the institution features for cluster formation and handle efficiently the arbitrary shaped clusters. Approach: The proposed work presented a fuzzy k-means cluster algorithm in the formation of student, faculty and infrastructural clusters based on the performance, skill set and facilitation availability respectively. The knowledge hidden among the educational data set is extracted through Fuzzy k-means cluster an unsupervised learning depends on certain initiation values to define the subgroups present in the data set. Results: Based on the features of the dataset and input parameters cluster formation vary, which motivates the clarification of cluster validity. The results of quality indexed fuzzy k-means shows better cluster validation compared to that of traditional k-family algorithm. Conclusion: The experimental results of cluster validation scheme confirm the reliability of validity index showing that it performs better than other k-family clusters.

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“…Belal et al (2005) proposed a new method for cluster initialization based on finding a set of medians extracted from a dimension with maximum variance. Zoubi et al (2008) proposed a new strategy to accelerate K-means clustering by avoiding unnecessary distance calculations through the partial distance logic. Fahim et al (2009) proposed a method to select a good initial solution by partitioning dataset into blocks and applying K-means to each block.…”

Section: Related Workmentioning

confidence: 99%

A hybridized K-means clustering approach for high dimensional dataset

Dash¹,

Mishra²,

Rath³

et al. 2010

Int. J. Eng. Sci. Tech

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Due to incredible growth of high dimensional dataset, conventional data base querying methods are inadequate to extract useful information, so researchers nowadays is forced to develop new techniques to meet the raised requirements. Such large expression data gives rise to a number of new computational challenges not only due to the increase in number of data objects but also due to the increase in number of features/attributes. Hence, to improve the efficiency and accuracy of mining task on high dimensional data, the data must be preprocessed by an efficient dimensionality reduction method. Recently cluster analysis is a popularly used data analysis method in number of areas. K-means is a well known partitioning based clustering technique that attempts to find a user specified number of clusters represented by their centroids. But its output is quite sensitive to initial positions of cluster centers. Again, the number of distance calculations increases exponentially with the increase of the dimensionality of the data. Hence, in this paper we proposed to use the Principal Component Analysis (PCA) method as a first phase for K-means clustering which will simplify the analysis and visualization of multi dimensional data set. Here also, we have proposed a new method to find the initial centroids to make the algorithm more effective and efficient. By comparing the result of original and new approach, it was found that the results obtained are more accurate, easy to understand and above all the time taken to process the data was substantially reduced.

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New Efficient Strategy to Accelerate k-Means Clustering Algorithm

Cited by 23 publications

References 16 publications

Clustering Algorithms in Echo State Networks

Clustering Algorithms in Echo State Networks

Fuzzy Modeled K-Cluster Quality Mining of Hidden Knowledge for Decision Support

A hybridized K-means clustering approach for high dimensional dataset

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