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Keywords
(9)
Cluster Algorithm
Data Clustering
Data Mining Algorithm
kmeans algorithm
Machine Learning
Mathematical Model
Numerical Calculation
Upper Bound
K Means
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Data clustering with modified Kmeans algorithm
Data clustering with modified Kmeans algorithm,10.1109/ICRTIT.2011.5972376,Ran Vijay Singh,M. P. S Bhatia
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Data clustering with modified Kmeans algorithm
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Ran Vijay Singh
,
M. P. S Bhatia
This paper presents a
data clustering
approach using modified
KMeans algorithm
based on the improvement of the sensitivity of initial center (seed point) of clusters. This algorithm partitions the whole space into different segments and calculates the frequency of data point in each segment. The segment which shows maximum frequency of data point will have the maximum probability to contain the centroid of cluster. The number of cluster's centroid (k) will be provided by the user in the same manner like the traditional Kmean algorithm and the number of division will be k*k ('k' vertically as well as 'k' horizontally). If the highest frequency of data point is same in different segments and the
upper bound
of segment crosses the threshold 'k' then merging of different segments become mandatory and then take the highest k segment for calculating the initial centroid (seed point) of clusters. In this paper we also define a threshold distance for each cluster's centroid to compare the distance between data point and cluster's centroid with this threshold distance through which we can minimize the computational effort during calculation of distance between data point and cluster's centroid. It is shown that how the modified kmean algorithm will decrease the complexity & the effort of numerical calculation, maintaining the easiness of implementing the k mean algorithm. It assigns the data point to their appropriate class or cluster more effectively. KeywordsKMeans, Data Clustering;
Conference:
International Conference on Recent Trends in Information Technology  ICRTIT
, 2011
DOI:
10.1109/ICRTIT.2011.5972376
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References
(11)
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Conference:
International Conference on Computational Intelligence and Software Engineering  CiSE
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(
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Conference:
Trendz in Information Sciences & Computing  TISC
, 2010
An Efficient Kmeans Clustering Algorithm Based on Influence Factors
(
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Conference:
Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing  SNPD
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Space Partitioning for Scalable KMeans
(
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Conference:
International Conference on Machine Learning and Applications  ICMLA
, pp. 319324, 2010
An Efficient kMeans Clustering Algorithm: Analysis and Implementation
(
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Journal:
IEEE Transactions on Pattern Analysis and Machine Intelligence  PAMI
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