A genetic k-medoids clustering algorithm

Sheng, Weiguo; Liu, Xiaohui

doi:10.1007/s10732-006-7284-z

Cited by 57 publications

(26 citation statements)

References 29 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The quality of clustering is estimated by a cost function, which can measure the average dissimilarity between the object and its referents [6]. The process is as follows [7], [8]:…”

Section: Basis Of Modelmentioning

confidence: 99%

Short-Term Electric Load Forecasting Based on Data Mining

Wu¹,

Niu²,

Song³

2017

IJET

View full text Add to dashboard Cite

Abstract-Short-term electric load forecasting is significant for safe and economic operation in power system. In order to improve the accuracy of predicted results, this paper proposes a PSO-SVM model, which is based on cluster analysis techniques and data accumulation pretreatment. Specifically, K-medoids cluster analysis is employed to extract similar days, then original data is divided into k category clusters. In addition, this paper carries on the accumulation of the original data to weaken the effect of the irregular disturbance and enhance the regularity of the sequence. And we use PSO to optimize the parameters of SVM system. In case analysis, the results show that the method proposed in this paper can effectively promote the forecasting performance.Index Terms-Short-term power load forecasting, data mining, K-medoids (KM), partial swam algorithm (PSO), support vector machine (SVM).

show abstract

“…The quality of clustering is estimated by a cost function, which can measure the average dissimilarity between the object and its referents [6]. The process is as follows [7], [8]:…”

Section: Basis Of Modelmentioning

confidence: 99%

Short-Term Electric Load Forecasting Based on Data Mining

Wu¹,

Niu²,

Song³

2017

IJET

View full text Add to dashboard Cite

show abstract

“…b.2.1 IP-based clustering grouping algorithm Current clustering algorithm like K-means [6] , k-medoids [7] and DBSCAN [8] does not work well on IP address. An algorithm that uses the longest prefix match [9] as the similarity metric and an adaptation of the nearest neighbor heuristic for clustering was presented in Ref.…”

Section: 2 Strategy-based Groupingmentioning

confidence: 99%

Research on grouping strategy of SIP-based streaming media P2P live broadcast network

Liu

Huang

2008

J. Electron.(China)

View full text Add to dashboard Cite

The rapid development of Internet has led to the explosion of information sharing, and how to supervise the sharing is a main research topic on current Internet. Aiming at the disadvantage that the current Peer-to-Peer (P2P) is hard to manage and control, this paper presents a Session Initial Protocol (SIP)-based P2P network of three-level architecture. SIP middleware is introduced to the middle level of the three-layer architecture. By the connection function of the SIP signaling, the P2P transmission on media-level can be controlled. Using SIP's register and authentication function, the manage layer can manage the whole P2P network. Based on the aforementioned architecture, this paper investigates the grouping strategy on a live broadcast application in P2P network. Combined with the function of SIP register, the paper works on several grouping strategies, sets up models to manage users by grouping them, presents a weight-based K-means IP address grouping algorithm, and realizes it. The experiment shows that the grouping strategy presented in this paper can solve the problem of group sharing of network resource, and can realize the efficient-sharing, reasonable-distributing of network resource.

show abstract

“…In order to improve the performance of k-medoid algorithm, many and various methods had been proposed. In (Sheng & Liu, 2006) a hybrid Genetic algorithm with k-medoid for clustering has been proposed, the new algorithm evolve appropriate partitioning while making no a priori assumption about the number of clusters present in the datasets. In (Archna, Pramod & Nair, 2010), the authors proposed an enhanced version for k-medoid that eliminate deficiency of the old one by calculating the initial medoid k as per needs of users and applies an effective approach for allocation of data points into the clusters.…”

Section: Introductionmentioning

confidence: 99%

An Improved Version of K-medoid Algorithm using CRO

Hudaib¹,

Khanafseh²,

Surakhi³

2018

MAS

View full text Add to dashboard Cite

Clustering is the process of grouping a set of patterns into different disjoint clusters where each cluster contains the alike patterns. Many algorithms had been proposed before for clustering. K-medoid is a variant of k-mean that use an actual point in the cluster to represent it instead of the mean in the k-mean algorithm to get the outliers and reduce noise in the cluster. In order to enhance performance of k-medoid algorithm and get more accurate clusters, a hybrid algorithm is proposed which use CRO algorithm along with k-medoid. In this method, CRO is used to expand searching for the optimal medoid and enhance clustering by getting more precise results.The performance of the new algorithm is evaluated by comparing its results with five clustering algorithms, k-mean, k-medoid, DB/rand/1/bin, CRO based clustering algorithm and hybrid CRO-k-mean by using four real world datasets: Lung cancer, Iris, Breast cancer Wisconsin and Haberman's survival from UCI machine learning data repository. The results were conducted and compared base on different metrics and show that proposed algorithm enhanced clustering technique by giving more accurate results.

show abstract

A genetic k-medoids clustering algorithm

Cited by 57 publications

References 29 publications

Short-Term Electric Load Forecasting Based on Data Mining

Short-Term Electric Load Forecasting Based on Data Mining

Research on grouping strategy of SIP-based streaming media P2P live broadcast network

An Improved Version of K-medoid Algorithm using CRO

Contact Info

Product

Resources

About