Clustering Biological Data Using Enhanced k-Means Algorithm

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

doi:10.1007/978-90-481-8776-8_37

Cited by 15 publications

(16 citation statements)

References 3 publications

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“…Usually these models are based on clustering or classification techniques and are used to find pattern from training set. Typical examples are clustering models [4,24,44], Kernel-mapping recommender [23,25,26], and Singular Value Decomposition (SVD) based models [22,63].…”

Section: Main Types Of Recommender Systemsmentioning

confidence: 99%

“…Abdul Nazeer et al [44] proposed an algorithm to solve time complexity and initial centroid selection issue of original k-means. The author modified two phases of k-means, where in phase one, pair-wise distance of data points are calculated and closest data point forms the cluster.…”

Section: Initial Centroid Selection In K-means Clusteringmentioning

confidence: 99%

“…The second phase is variant of [30] which takes the resulting initial centroids of phase one as input. 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.…”

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: Main Types Of Recommender Systemsmentioning

confidence: 99%

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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show abstract

“…In order to test our clustering algorithm OPE-HCA and other well-known clustering algorithm, including K-means [6], fuzzy c-means (FCM) [10], BIRCH [11], and Bayesian hierarchical clustering (BHC) [15], the class label attributes in the datasets are completely deleted. The reason why we choose K-means, FCM, BIRCH, and BHC as the comparing algorithms with our algorithm is that our proposed algorithm combines hierarchical idea, distance-based method with probabilistic computation.…”

Section: Compared Algorithmsmentioning

confidence: 99%

“…All seeds are then improved iteratively by the designed criterion functions. The typical representatives of partitional clustering methods include K-means clustering and its variations [4][5][6], K-medoids clustering [7,8], and fuzzy c-means clustering [9,10]. Although K-means-type clustering has inherent limitations, such as subjective determination of the number of clusters and local convergence, this type of algorithms is applied widely in many data domains due to their simplicity and understandability and also has good performances in many cases.…”

Section: Related Workmentioning

confidence: 99%

OPE-HCA: an optimal probabilistic estimation approach for hierarchical clustering algorithm

Fan

2015

Neural Comput & Applic

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The Survival of the Fittest is a principle which selects the superior and eliminates the inferior in the nature. This principle has been used in many fields, especially in optimization problem-solving. Clustering in data mining community endeavors to discover unknown representations or patterns hidden in datasets. Hierarchical clustering algorithm (HCA) is a method of cluster analysis which searches the optimal distribution of clusters by a hierarchical structure. Strategies for hierarchical clustering generally have two types: agglomerative with a bottom-up procedure and divisive with a top-down procedure. However, most of the clustering approaches have two disadvantages: the use of distance-based measurement and the difficulty of the clusters integration. In this paper, we propose an optimal probabilistic estimation (OPE) approach by exploiting the Survival of the Fittest principle. We devise a hierarchical clustering algorithm (HCA) based on OPE, also called OPE-HCA. The OPE-HCA combines optimization with probability and agglomerative HCA. Experimental results show that the OPE-HCA has the ability of searching and discovering patterns at different description levels and can also obtain better performance than many clustering algorithms according to NMI and clustering accuracy measures.

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A New Hybrid K-Means Evolving Spiking Neural Network Model Based on Differential Evolution

Saleh

Hamed

Shamsuddin

et al. 2017

Recent Trends in Information and Communication Technology

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Clustering Biological Data Using Enhanced k-Means Algorithm

Cited by 15 publications

References 3 publications

Novel centroid selection approaches for KMeans-clustering based recommender systems

Novel centroid selection approaches for KMeans-clustering based recommender systems

OPE-HCA: an optimal probabilistic estimation approach for hierarchical clustering algorithm

A New Hybrid K-Means Evolving Spiking Neural Network Model Based on Differential Evolution

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