Clustering Quality Improvement of k-means Using a Hybrid Evolutionary Model

Karimov, Jeyhun; Özbayoğlu, A. Murat

doi:10.1016/j.procs.2015.09.143

Cited by 26 publications

(11 citation statements)

References 11 publications

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“…Selecting appropriate candidates for the initial centroids is essential for clustering quality and the performance. Authors in the paper [16] have proposed a hybrid evolutionary model. It has Meta heuristic methods to identify the appropriate candidates for initial centroids in k-means.…”

Section: Related Workmentioning

confidence: 99%

Efficient Clustering Algorithm with Enhanced Cohesive Quality Clusters

Khandare¹,

Alvi²

2018

IJISA

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Analyzing data is a challenging task nowadays because the size of data affects results of the analysis. This is because every application can generate data of massive amount. Clustering techniques are key techniques to analyze the massive amount of data. It is a simple way to group similar type data in clusters. The key examples of clustering algorithms are k-means, kmedoids, c-means, hierarchical and DBSCAN. The kmeans and DBSCAN are the scalable algorithms but again it needs to be improved because massive data hampers the performance with respect to cluster quality and efficiency of these algorithms. For these algorithms, user intervention is needed to provide appropriate parameters as an input. For these reasons, this paper presents modified and efficient clustering algorithm. This enhances cluster's quality and makes clusters more cohesive using domain knowledge, spectral analysis, and split-merge-refine techniques. Also, this algorithm takes care to minimizing empty clusters. So far no algorithm has integrated these all requirements that proposed algorithm does just as a single algorithm. It also automatically predicts the value of k and initial centroids to have minimum user intervention with the algorithm. The performance of this algorithm is compared with standard clustering algorithms on various small to large data sets. The comparison is with respect to a number of records and dimensions of data sets using clustering accuracy, running time, and various clusters validly measures. From the obtained results, it is proved that performance of proposed algorithm is increased with respect to efficiency and quality than the existing algorithms.

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Section: Related Workmentioning

confidence: 99%

Efficient Clustering Algorithm with Enhanced Cohesive Quality Clusters

Khandare¹,

Alvi²

2018

IJISA

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“…In the paper [14], authors present new centroids initialization approach to improving the basic k-means algorithm with high-quality clusters. Authors in papers [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] have tried to improve the clustering algorithms which are used in various domains like networking and biometrics. However, these algorithms can be improved further.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Improved Clustering Algorithm on Real and Synthetic Data

Khandare¹,

Alvi²

2017

IJCNIS

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Abstract-Clustering is an important technique in data mining to partition the data objects into clusters. It is a way to generate groups from the data objects. Different data clustering methods or algorithms are discussed in the various literature. Some of these are efficient while some are inefficient for large data. The k-means, Partition Around Method (PAM) or k-medoids, hierarchical and DBSCAN are various clustering algorithms. The k-means algorithm is more popular than the other algorithms used to partition data into k clusters. For this algorithm, k should be provided explicitly. Also, initial means are taken randomly but this may generate clusters with poor quality. This paper is a study and implementation of an improved clustering algorithm which automatically predicts the value of k and uses a new technique to take initial means. The performance analysis of the improved algorithm and other algorithms by using real and dummy datasets is presented in this paper. To measure the performance of algorithms, this paper uses running time of algorithms and various cluster validity measures. Cluster validity measures include sum squared error, silhouette score, compactness, separation, Dunn index and DB index. Also, the k predicted by the improved algorithm is compared with optimal k suggested by elbow method. It is found that both values of k are almost similar. Most of the values of validity measures for the improved algorithm are found to be optimal.

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“…The amount of clusters for this technique should be predefined and the algorithms used in this approach are K-Means Algorithm, K-Medoid Algorithm, K-Nearest Neighbour Algorithm etc [4].…”

Section: Partitioning Based Clusteringmentioning

confidence: 99%

Comparative Study of Initial Centroid Based K-Mode Algorithm

2017

IJAERD

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Clustering Quality Improvement of k-means Using a Hybrid Evolutionary Model

Cited by 26 publications

References 11 publications

Efficient Clustering Algorithm with Enhanced Cohesive Quality Clusters

Efficient Clustering Algorithm with Enhanced Cohesive Quality Clusters

Performance Analysis of Improved Clustering Algorithm on Real and Synthetic Data

Comparative Study of Initial Centroid Based K-Mode Algorithm

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