نتایج جستجو برای: k-means algorithm

تعداد نتایج: 1381711  

Journal: :journal of computer and robotics 0
rasool azimi faculty of computer and information technology engineering, qazvin branch, islamic azad university, qazvin, iran hedieh sajedi department of computer science, college of science, university of tehran, tehran, iran

identifying clusters or clustering is an important aspect of data analysis. it is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. it is a main task of exploratory data mining, and a common technique for statistical data analysis this paper proposed an improved version of k-means algorithm, namely persistent k...

Hedieh Sajedi Rasool Azimi

Identifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means algorithm, namely Persistent K...

Journal: :IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 1999

Journal: :Mathematical Problems in Engineering 2014

Journal: :Theor. Comput. Sci. 2011
Tobias Brunsch Heiko Röglin

k-means++ is a seeding technique for the k-means method with an expected approximation ratio of O(log k), where k denotes the number of clusters. Examples are known on which the expected approximation ratio of k-means++ is Ω(log k), showing that the upper bound is asymptotically tight. However, it remained open whether k-means++ yields an O(1)-approximation with probability 1/poly(k) or even wi...

Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering algorithms in many fields, a lot of research is still going on to find the best and efficient clustering algorithm. K-means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper...

Journal: :Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 2004

Journal: :Jurnal Transformatika 2010

Journal: :Computational Statistics 2007

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