Near-Optimal Centroids Initialization in K-means Algorithm Using K-Bees Algorithm
نویسندگان
چکیده
The K-mean algorithm is one of the popular clustering techniques. The algorithm requires user to state and initialize centroid values of each group in advance. This creates problem for novice users especially to those who have no or little knowledge on the data. Trial-error attempt might be one of the possible preference to deal with this issue. In this paper, an optimization algorithm inspired from the bees foraging activities is used to locate near-optimal centroid of a given data set. Result shows that propose approached prove it robustness and competence in finding a near optimal centroid on both synthetic and real data sets.
منابع مشابه
A Hybrid Data Clustering Algorithm Using Modified Krill Herd Algorithm and K-MEANS
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...
متن کاملImproved COA with Chaotic Initialization and Intelligent Migration for Data Clustering
A well-known clustering algorithm is K-means. This algorithm, besides advantages such as high speed and ease of employment, suffers from the problem of local optima. In order to overcome this problem, a lot of studies have been done in clustering. This paper presents a hybrid Extended Cuckoo Optimization Algorithm (ECOA) and K-means (K), which is called ECOA-K. The COA algorithm has advantages ...
متن کاملEfficient and Fast Initialization Algorithm for K- means Clustering
The famous K-means clustering algorithm is sensitive to the selection of the initial centroids and may converge to a local minimum of the criterion function value. A new algorithm for initialization of the K-means clustering algorithm is presented. The proposed initial starting centroids procedure allows the K-means algorithm to converge to a “better” local minimum. Our algorithm shows that ref...
متن کاملAn Effective and Efficient Algorithm for Document Clustering
This paper proposes an effective and efficient algorithm for clustering text documents. This algorithm is formulated by using the concept of well known k-means algorithm. The standard k-means algorithm suffers from the problem of random initialization of initial cluster centers. The proposed algorithm eliminates this problem by introducing a new approach for selection of initial cluster centroi...
متن کاملA Hybrid K-harmonic Means with Abcclustering Algorithm Using an Optimal K Value for High Performance Clustering
Large quantities of data are emerging every year and an accurate clustering algorithm is needed to derive information from these data. K-means clustering algorithm is popular and simple, but has many limitations like its sensitivity to initialization, provides local optimum solutions. K-harmonic means clustering is an improved variant of K-means which is insensitive to the initialization of cen...
متن کامل