.png){kind=icon}
.png){kind=icon}
Return
Efficient protocol for data clustering by fuzzy Cuckoo Optimization Algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2015.12.008.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Data clustering is a technique for grouping similar and dissimilar data. Many clustering algorithms fail when dealing with multi-dimensional data. This paper introduces efficient methods for data clustering by Cuckoo Optimization Algorithm; called COAC and Fuzzy Cuckoo Optimization Algorithm, called FCOAC. The COA by inspire of cuckoo bird nature life tries to solve continuous problems. This algorithm clusters a large dataset to prior determined clusters numbers by this meta-heuristic algorithm and optimal the results by fuzzy logic. Firstly, the algorithm generates a random solutions equal to cuckoo population and with length dataset objects and with a cost function calculates the cost of each solution. Finally, fuzzy logic tries for the optimal solution. The performance of our algorithm is evaluated and compared with COAC, Black hole, CS, K-mean, PSO and GSA. The results show that our algorithm has better performance in comparison with them. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Data clustering
Cuckoo Optimization Algorithm (COA)
Dataset
Fuzzy logic
Artificial intelligence
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.