PERSISTENT K-MEANS: STABLE DATA CLUSTERING ALGORITHM BASED ON K-MEANS ALGORITHM

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AZIMI RASOOL; SAJEDI HEDIEH

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Journal Paper

Paper Information

Journal: JOURNAL OF COMPUTER AND ROBOTICS Year:2014 | Volume:7 | Issue:1 Page(s): 57-66

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Information Journal Paper

Title

PERSISTENT K-MEANS: STABLE DATA CLUSTERING ALGORITHM BASED ON K-MEANS ALGORITHM

Author(s)

AZIMI RASOOL | SAJEDI HEDIEH | Issue Writer Certificate

Keywords

DATA MINING

CLUSTERING

K-MEANS

PERSISTENT K-MEANS

Abstract

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-MEANS, which alters the convergence method of K-MEANS algorithm to provide more accurate CLUSTERING results than the K-MEANS algorithm and its variants by increasing the clusters’ coherence. Persistent K-MEANS uses an iterative approach to discover the best result for consecutive iterations of KMeans algorithm.

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APA: Copy

AZIMI, RASOOL, & SAJEDI, HEDIEH. (2014). PERSISTENT K-MEANS: STABLE DATA CLUSTERING ALGORITHM BASED ON K-MEANS ALGORITHM. JOURNAL OF COMPUTER AND ROBOTICS, 7(1), 57-66. SID. https://sid.ir/paper/685325/en

Vancouver: Copy

AZIMI RASOOL, SAJEDI HEDIEH. PERSISTENT K-MEANS: STABLE DATA CLUSTERING ALGORITHM BASED ON K-MEANS ALGORITHM. JOURNAL OF COMPUTER AND ROBOTICS[Internet]. 2014;7(1):57-66. Available from: https://sid.ir/paper/685325/en

IEEE: Copy

RASOOL AZIMI, and HEDIEH SAJEDI, “PERSISTENT K-MEANS: STABLE DATA CLUSTERING ALGORITHM BASED ON K-MEANS ALGORITHM,” JOURNAL OF COMPUTER AND ROBOTICS, vol. 7, no. 1, pp. 57–66, 2014, [Online]. Available: https://sid.ir/paper/685325/en

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