An efficient enhanced k-means clustering algorithm

Fahim, Ahmed; Salem, Abdel-Badeeh M.; Torkey, Fawzy A.; Ramadan, Mohamed A.

doi:10.1631/jzus.2006.a1626

Cited by 262 publications

(119 citation statements)

References 7 publications

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“…The Memory -Intensive approaches as in [12,13] involve storing some informat ion in each iteration to be used in the future iterations. These approaches start with keeping track of as to for how many iterat ions a point has not changed its cluster and removes the point when stability is detected.…”

Section: B Approaches To Reduce Cost Per Iteration In K-meansmentioning

confidence: 99%

“…These approaches start with keeping track of as to for how many iterat ions a point has not changed its cluster and removes the point when stability is detected. Authors in [12] p roposed that instead of calculat ing distance of a data point fro m all centers, more effect ive step would be to limit these iterations to consider only the distance fro m the nearest center keeping in memory the results fro m the prev ious iterations. The first iteration would include calculat ing the distance fro m the nearest cluster and the successive iteration will co mpute the distance from the previous nearest cluster.…”

Section: B Approaches To Reduce Cost Per Iteration In K-meansmentioning

confidence: 99%

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Accelerated K-means Clustering Algorithm

Jain¹,

Buksh²

2016

IJITCS

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Abstract-Optimizing K-means is still an active area of research for purpose of clustering. Recent developments in Cloud Co mputing have resulted in emergence of Big Data Analytics. There is a fresh need of simp le, fast yet accurate algorithm for clustering huge amount of data. This paper proposes optimization of K-means through reduction of the points which are considered for reclustering in each iteration. The work is generalizat ion of earlier work by Poteras et al who proposed this idea. The suggested scheme has an improved average runtime. The cost per iteration reduces as number of iterations grow which makes the proposal very scalable.

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Section: B Approaches To Reduce Cost Per Iteration In K-meansmentioning

confidence: 99%

Section: B Approaches To Reduce Cost Per Iteration In K-meansmentioning

confidence: 99%

Accelerated K-means Clustering Algorithm

Jain¹,

Buksh²

2016

IJITCS

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“…An efficient enhanced Kmeans method is proposed in [11] for assigning data points to clusters. The original K-means algorithm is having high time complexity because each iteration computes the distances between data points and all the centroids.…”

Section: Related Workmentioning

confidence: 99%

Sum of Distance based Algorithm for Clustering Web Data

Arora¹,

Motwani²

2014

IJCA

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Clustering is a data mining technique used to make groups of objects that are somehow similar in characteristics. The criterion for checking the similarity is implementation dependent.Clustering analyzes data objects without consulting a known class label or category i.e. it is an unsupervised data mining technique. K-means is a widely used clustering algorithm that chooses random cluster centers (centroid), one for each centroid. The performance of K-means strongly depends on the initial guess of centers (centroid) and the final cluster centroids may not be the optimal ones as the algorithm can converge to local optimal solutions. Therefore it is important for K-means to have good choice of initial centroids. An algorithm for clustering that selects initial centroids using criteria of finding sum of distances of data objects to all other data objects have been formed. The proposed algorithm results in better clustering on synthetic as well as real datasets when compared to the K-means technique.

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“…This algorithm was improved upon by G-means [24], and PG means [25]. In addition, Fahim et al [26], developed overlapped and enhanced k-means and evaluated them with Wind, Letter and Abalone data.…”

Section: Introductionmentioning

confidence: 99%