Enhancing the K-means Clustering Algorithm by Using a O(n logn) Heuristic Method for Finding Better Initial Centroids

Nazeer, K. A. Abdul; Kumar, Sunil; Sebastian, M. P.

doi:10.1109/eait.2011.57

Cited by 24 publications

(15 citation statements)

References 7 publications

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“…k-means randomly chooses initial centers therefore it does not guarantee to produce unique clustering results. Initial centroid selection not only influences the efficiency of the algorithm but also the number of iterations desired to run the original k-means algorithm [45]. Though k-means is known for its intelligence to cluster large datasets but its computation complexity is very expensive for massive data sets [45].…”

Section: Initial Centroid Selection In K-means Clusteringmentioning

confidence: 99%

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Novel centroid selection approaches for KMeans-clustering based recommender systems

Zahra

Ghazanfar

Khalid

et al. 2015

Information Sciences

198

110

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Recommender systems have the ability to filter unseen information for predicting whether a particular user would prefer a given item when making a choice. Over the years, this process has been dependent on robust applications of data mining and machine learning techniques, which are known to have scalability issues when being applied for recommender systems. In this paper, we propose a k-means clustering-based recommendation algorithm, which addresses the scalability issues associated with traditional recommender systems. An issue with traditional k-means clustering algorithms is that they choose the initial k centroid randomly, which leads to inaccurate recommendations and increased cost for offline training of clusters. The work in this paper highlights how centroid selection in k-means based recommender systems can improve performance as well as being cost saving. The proposed centroid selection method has the ability to exploit underlying data correlation structures, which has been proven to exhibit superior accuracy and performance in comparison to the traditional centroid selection strategies, which choose centroids randomly. The proposed approach has been validated with an extensive set of experiments based on five different datasets (from movies, books, and music domain). These experiments prove that the proposed approach provides a better quality cluster and converges quicker than existing approaches, which in turn improves accuracy of the recommendation provided.

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Section: Initial Centroid Selection In K-means Clusteringmentioning

confidence: 99%

“…The algorithm proposed by [44] produces better quality clusters in less amount of time. This algorithm has further been enhanced in [45] by using heuristic approach. Firstly, all the data points are sorted in ascending order and then divided in k sets.…”

Section: Initial Centroid Selection In K-means Clusteringmentioning

confidence: 99%

Novel centroid selection approaches for KMeans-clustering based recommender systems

Zahra

Ghazanfar

Khalid

et al. 2015

Information Sciences

198

110

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“…In their recent work [8] K A Abdul Nazeer and et al proposed heuristic based method. The basic idea of this algorithm is to determine the initial centroids of the clusters in a heuristic manner, so as to ensure that the centroids are chosen in accordance with the distribution of data.…”

Section: Related Workmentioning

confidence: 99%

“…The three initialization methods explored are K-means with weighted average method [4], Principal component analysis [5][6][7] and a heuristic method [8] based on sorting and partitioning of the input data for finding better initial centroids. Experimental results show that the proposed algorithms produce better clusters in less computational time by parallelizing the tasks using Hadoop cluster setup.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Centroid Selection Strategies for Distributed K-means Clustering Algorithm

Ghuli¹,

Prabhakar²,

Shettar³

2015

IJCA

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Extremely large data sets often known as "Big Data" are analyzed for interesting patterns, trends, and associations, especially those relating to human behavior and interactions. Extraction of meaningful and useful information needs to be done in parallel using advanced clustering algorithms. In this paper, effort has been made to tweak in changes to the existing K-means algorithm so as to work in parallel using MapReduce paradigm. K-means due to its gradient descent nature is highly sensitive to the initial placement of the cluster centers. This random initialization of cluster centers results in empty clusters and slower convergence. In this paper, an overview of existing methods with emphasis on computational efficiency is presented. Comparison of three well known linear time complexity initialization methods has been presented here. These methods are analyzed on two different data sets. The experimental results are recorded and presented with insights on different initialization methods for practitioners.

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“…[1] Presented an updated k-means that incorporates arranging the data set and allocating arranged data set into "k" number of sets which, brought about better beginning centroids thusly upgrading the accuracy of this calculation. The calculation focalizes speedier stood out from customary calculation of K-Means.…”

Section: Introductionmentioning

confidence: 99%

Clustering Analysis for Appropriate Crop Production Using Hierarchical, Fuzzy C-Means, K-Means and Model Based Techniques

2017

IJAERD

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Enhancing the K-means Clustering Algorithm by Using a O(n logn) Heuristic Method for Finding Better Initial Centroids

Cited by 24 publications

References 7 publications

Novel centroid selection approaches for KMeans-clustering based recommender systems

Novel centroid selection approaches for KMeans-clustering based recommender systems

A Comprehensive Survey on Centroid Selection Strategies for Distributed K-means Clustering Algorithm

Clustering Analysis for Appropriate Crop Production Using Hierarchical, Fuzzy C-Means, K-Means and Model Based Techniques

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