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Parham Moradi

Parham Moradi

Academic rank: Associate Professor
ORCID:
Education: PhD.
ScopusId: 654
Faculty: Faculty of Engineering
Address: Department of Computer Engineering, Faculty of Engineering, University of Kurdistan
Phone:

Research

Title
A Feature Weighting Based Artificial Bee Colony Algorithm for Data Clustering
Type
Presentation
Keywords
Data clustering; Artificial bee colony algorithm;feature weigthing
Year
2016
Researchers Manijeh Rahimi ، Parham Moradi ، Alireza Abdollahpouri

Abstract

Data clustering is a powerful technique for data analysis that used in many applications. The goal of clustering is to detect groups that objects of each group have the most similarity together. Artificial bee colony (ABC) is a simple algorithm with few control parameters to solve clustering problem. However, traditional ABC algorithm is considered the equal importance for all features, while real world applications carry different importance on features. To overcome this issue, we proposed a feature weighting based artificial bee colony (FWABC) algorithm for data clustering. The proposed algorithm considers a specific importance to each feature. The performance of the proposed method has been tested on various datasets and compared to well-known and state-of-the-art methods, the reported results show that the proposed method outperforms other methods.