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Register a new userA New Algorithm for Data Clustering and Enhancing K-Means Algorithm
خوارزمية جديدة لعنقدة البيانات و تحسين خوارزمية الK-Means
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نقدم في هذا البحث خوارزمية جديدة لحل بعض المشاكل التي تعاني منها خوارزميات عنقدة البيانات كالK-Means. هذه الخوارزمية الجديدة قادرة على عنقدة مجموعة من البيانات بشكل منفرد دون الحاجة لخوارزميات عنقدة أخرى.
This paper introduces a new algorithm to solve some problems that data clustering algorithms such as K-Means suffer from. This new algorithm by itself is able to cluster data without the need of other clustering algorithms.
References used
HAN, J, AND KAMBER, M. 2006- Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, New Delhi, (2nd ed), 772p
RAUF, A, SHEEBA, S, KHUSOR, S, AND JAVED, H.2012- Enhanced K-Mean Clustering Algorithm to Reduce Number of Iterations and Time Complexity, Middle-East Journal of Scientific Research, Pakistan
ALARBEA, A, SENTHEKUMAR, H, AND BADER, A. 2013- Enhancing K-Means Algorithm with Initial Cluster Centers Derived from Data Partitioning along the Data Axis with PCA, Journal of Advances in Computer Networks, Vol. 1, No. 2, June 2013
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The algorithm classifies objects to a predefined number of clusters, which is given by the user (assume k clusters). The idea is to choose random cluster centers, one for each cluster. These centers are preferred to be as far as possible from each ot
her. Starting points affect the clustering process and results. Here the Centroid initialization plays an important role in determining the cluster assignment in effective way. Also, the convergence behavior of clustering is based on the initial centroid values assigned. This research focuses on the assignment of cluster centroid selection so as to improve the clustering performance by K-Means clustering algorithm. This research uses Initial Cluster Centers Derived from Data Partitioning along the Data Axis with the Highest Variance to assign for cluster centroid.
In this paper, we introduce a modification to fuzzy mountain
data clustering algorithm. We were able to make this algorithm
working automatically, through finding a way to divide the
space, to determine the values of the input parameters, and
the stop condition automatically, instead of getting them by the
user.
In this paper, we introduce a modification to fuzzy mountain
data clustering algorithm. We were able to make this algorithm
working automatically, through finding a way to divide the
space, to determine the values of the input parameters, and
the stop condition automatically, instead of getting them by the
user.
The majority of recent digital signature algorithms depend, in their
structure, on complicated mathematical concepts that require a long
time and a significant computational effort to be executed. As a
trial to reduce these problems, some researchers have proposed
digital signature algorithms which depend on simple arithmetic
functions and operations that are executed quickly, but that was at
the expense of the security of algorithms.
As it’s known, The Graph k-Colorability Problem (GCP) is a wellknown
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that any two adjoined vertices, which are connecte
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graph in such a way that any two edges joined by a vertex have always
different colors? In this paper we introduce a new effective algorithm for
coloring the edges of the graph. Our proposed algorithm enables us to
achieve a Continuously Edge Coloring (CEC) for a set of known graphs.
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