Generalization Threshold Optimization of Fuzzy Rough Set algorithm in Healthcare Data Classification

Beibei Dong,Yu Liu,Benzhen Guo,Xiao Zhang 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.3

There is ineffective classification problem in application of K-means clustering algorithm in massive data cluster analysis. This paper presents a K-means algorithm based on generalization threshold rough set optimization weight. Firstly, utilize attribute order described method, using the average distance calculation with Laplace method to optimize the generalization threshold of fuzzy rough set , then the Euclidean distance metric is used in the calculation of the similarity of K-means algorithm, introducing the variation coefficient into the cluster analysis, clustering the Euclidean distance weighted K-means algorithm totally based on data, finally, combine the rough set algorithm based on the generalization threshold optimization and K-means clustering algorithm, applied to medical and health data classification. The K-means algorithm based on generalization threshold rough set optimization weight presented by this paper has a better effect on medical and health data classification.

클러스터 중심 결정 방법을 개선한 K-Means 알고리즘의 구현

이신원,오형진,안동언,정성종,Lee Shin-Won,Oh HyungJin,An Dong-Un,Jeong Seong-Jong 한국정보처리학회 2004 정보처리학회논문지B Vol.11 No.7

K-Means algorithm is a non-hierarchical (plat) and reassignment techniques and iterates algorithm steps on the basis of K cluster centroids until the clustering results converge into K clusters. In its nature, K-Means algorithm has characteristics which make different results depending on the initial and new centroids. In this paper, we propose the modified K-Means algorithm which improves the initial and new centroids decision methodologies. By evaluating the performance of two algorithms using the 16 weighting scheme of SMART system, the modified algorithm showed $20{\%}$ better results on recall and F-measure than those of K-Means algorithm, and the document clustering results are quite improved. K-Means 알고리즘은 재배치 기법의 일종으로 K개의 초기 센트로이드를 중심으로 K개의 클러스터가 될 때까지 클러스터링을 반복하는 것이다. 알고리즘의 특성상 K-Means 알고리즘은 초기 클러스터 센트로이드(중심) 및 클러스터 중심을 결정하는 방법에 따라 다른 클러스터링 결과를 얻을 수 있다. 본 논문에서는 K-Means 알고리즘을 이용한 초기 클러스터 중심 및 클러스터 중심을 결정하는 방법을 개선한 변형 K-Means 알고리즘을 제안한다. 제안한 알고리즘의 평가를 위하여 SMART 시스템의 16가지 가중치 계산 방식을 이용하여 성능을 평가한 결과 변형 K-Means알고리즘이 K-Means 알고리즘보다 재현률과 F-Measure에서 $20{\%}$이상 향상된 결과를 얻을 수 있었으며 특정 주제 아래 관련 문서가 할당되는 클러스터링 성능이 우수함을 알 수 있었다.

Approximate k values using Repulsive Force without Domain Knowledge in k-means

( Jung-jae Kim ),( Minwoo Ryu ),( Si-ho Cha ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.3

The k-means algorithm is widely used in academia and industry due to easy and simple implementation, enabling fast learning for complex datasets. However, k-means struggles to classify datasets without prior knowledge of specific domains. We proposed the repulsive k-means (RK-means) algorithm in a previous study to improve the k-means algorithm, using the repulsive force concept, which allows deleting unnecessary cluster centroids. Accordingly, the RK-means enables to classifying of a dataset without domain knowledge. However, three main problems remain. The RK-means algorithm includes a cluster repulsive force offset, for clusters confined in other clusters, which can cause cluster locking; we were unable to prove RK-means provided optimal convergence in the previous study; and RK-means shown better performance only normalize term and weight. Therefore, this paper proposes the advanced RK-means (ARK-means) algorithm to resolve the RK-means problems. We establish an initialization strategy for deploying cluster centroids and define a metric for the ARK-means algorithm. Finally, we redefine the mass and normalize terms to close to the general dataset. We show ARK-means feasibility experimentally using blob and iris datasets. Experiment results verify the proposed ARK-means algorithm provides better performance than k-means, k’-means, and RK-means.

맵리듀스를 이용한 다중 중심점 집합 기반의 효율적인 클러스터링 방법

강성민(Sungmin Kang),이석주(Seokjoo Lee),민준기(Jun-ki Min) 한국정보과학회 2015 정보과학회 컴퓨팅의 실제 논문지 Vol.21 No.7

데이터 사이즈가 증가함에 따라서 대용량 데이터를 분석하여 데이터의 특성을 파악하는 것이 매우 중요해졌다. 본 논문에서는 분산 병렬 처리 프레임워크인 맵리듀스를 활용한 k-Means 클러스터링 기반의 효과적인 클러스터링 기법인 MCSK-Means (Multi centroid set k-Means)알고리즘을 제안한다. k-Means 알고리즘은 임의로 정해지는 k개의 초기 중심점들의 위치에 따라서 클러스터링 결과의 정확도가 많은 영향을 받는 문제점을 가지고 있다. 이러한 문제를 해결하기 위하여, 본 논문에서 제안하는 MCSK-Means 알고리즘은 k개의 중심점들로 이루어진 m개의 중심점 집합을 사용하여 임의로 생성되는 초기 중심점의 의존도를 줄였다. 또한, 클러스터링 단계를 거친 m개의 중심점 집합들에 속한 중심점들에 대하여 직접 계층 클러스터링 알고리즘을 적용하여 k개의 클러스터 중심점들을 생성하였다. 본 논문에서는 MCSK-Means 알고리즘을 맵리듀스 프레임워크 환경에서 개발하여 대용량 데이터를 효율적으로 처리할 수 있도록 하였다. As the size of data increases, it becomes important to identify properties by analyzing big data. In this paper, we propose a k-Means based efficient clustering technique, called MCSKMeans (Multi centroid set k-Means), using distributed parallel processing framework MapReduce. A problem with the k-Means algorithm is that the accuracy of clustering depends on initial centroids created randomly. To alleviate this problem, the MCSK-Means algorithm reduces the dependency of initial centroids using sets consisting of k centroids. In addition, we apply the agglomerative hierarchical clustering technique for creating k centroids from centroids in m centroid sets which are the results of the clustering phase. In this paper, we implemented our MCSK-Means based on the MapReduce framework for processing big data efficiently.

A Network Intrusion Detection Model Based on K-means Algorithm and Information Entropy

Gao Meng,Li Dan,Wang Ni-hong,Liu Li-chen 보안공학연구지원센터 2014 International Journal of Security and Its Applicat Vol.8 No.6

Many factors could influence the clustering performance of K-means algorithm, selection of initial cluster centers was an important one, traditional method had a certain degree of randomness in dealing with this problem, for this purpose, information entropy was introduced into the process of cluster centers selection, and a fusion algorithm combining with information entropy and K-means algorithm was proposed, in which, information entropy value was used to measure the similarity degree among records, the least similar record would be regarded as a cluster center. In addition, a network intrusion detection model was built, it could make cluster centers change dynamically along with the network changes, and the model could real-time update the cluster centers according to actual needs. Experiment results show that the improved algorithm proposed is better than the traditional K-means algorithm in detection ratio and false alarm ratio, and the network intrusion detection model is proved to be feasible.

A New Clustering Algorithm of Hybrid Strategy Optimization

Li Yi-ran,Zhang Chun-na 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.7

Normally, improving the performance of clustering depends on improvement of the algorithm. On the basis, this paper presents a hybrid strategy optimization algorithm that K-means algorithm effectively combined with PSO algorithm, which not only has played their respective advantages, but also reflected a hybrid performance. First of all, combined with a semi-supervised clustering idea, to optimize the clustering center of particle by K - means in the iteration of algorithm, enhanced the searching capability of the particles. Secondly, improved the traditional K - means enhance the ability of the algorithm to deal with the concave and convex points. Finally, the algorithm is introduced into the particle state determination mechanism, on implementing mutation for unstable particles, so that the algorithm to obtain stable performance. Experimental results show that the hybrid algorithm optimization ability is outstanding, and the convergence and stability can be effectively improved.

Liver Function Diagnosis Based on Artificial Bee Colony and K-Means Algorithm

Zhang Lin,Li Peng,Qiao Pei-li 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.1

The traditional K-Means clustering is sensitive to random selection of initial cluster centroids, easily into the local optimal solution. In this paper, an efficient aggregation algorithm which combined with Artificial bee colony and K-Means algorithm is proposed to apply to the diagnosis of liver function. The algorithm reduced the dependence on the initial cluster centroids and the probability to be trapped by local optimal solution, thus assigning data points to their appropriate cluster more efficient. The experimental results show that algorithm proposed in this paper is superior to the K-Means clustering in diagnosis of liver function.

Initial Mode Decision Method for Clustering in Categorical Data

양순철,강형창,김철수 한국데이터정보과학회 2007 한국데이터정보과학회지 Vol.18 No.2

The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. The k-modes algorithm is to extend the k-means paradigm to categorical domains. The algorithm requires a pre-setting or random selection of initial points (modes) of the clusters.This paper improved the problem of k-modes algorithm, using the Max-Min method that is a kind of methods to decide initial values in k-means algorithm. we introduce new similarity measures to deal with using the categorical data for clustering.We show that the mushroom data sets and soybean data sets tested with the proposed algorithm has shown a good performance for the two aspects(accuracy, run time).

다목적 유전자 알고리즘을 이용한문서 클러스터링

이정송(Jung Song Lee),박순철(Soon Cheol Park) 한국산업정보학회 2012 한국산업정보학회논문지 Vol.17 No.2

본 논문에서는 텍스트 마이닝 분야에서 중요한 부분을 차지하고 있는 문서 클러스터링을 위하여 다목적 유전자 알고리즘을 제안한다. 문서 클러스터링에 있어 중요한 요소 중 하나는 유사한 문서를 그룹화 하는 클러스터링 알고리즘이다. 지금까지 문서 클러스터링에는 k-means 클러스터링, 유전자 알고리즘 등을 사용한 연구가 많이 진행되고 있다. 하지만 k-means 클러스터링은 초기 클러스터 중심에 따라 성능 차이가 크며 유전자 알고리즘은 목적 함수에 따라 지역 최적해에 쉽게 빠지는 단점을 갖고 있다. 본 논문에서는 이러한 단점을 보완하기 위하여 다목적 유전자 알고리즘을 문서 클러스터링에 적용해 보고, 기존의 알고리즘과 정확성을 비교 및 분석한다. 성능 시험을 통해 k-means 클러스터링(약 20%)과 기존의 유전자 알고리즘(약 17%)을 비교할 때 본 논문에서 제안한 다목적 유전자 알고리즘의 성능이 월등하게 향상됨을 보인다. In this paper, the multi-objective genetic algorithm is proposed for the document clustering which is important in the text mining field. The most important function in the document clustering algorithm is to group the similar documents in a corpus. So far, the k-means clustering and genetic algorithms are much in progress in this field. However, the k-means clustering depends too much on the initial centroid, the genetic algorithm has the disadvantage of coming off in the local optimal value easily according to the fitness function. In this paper, the multi-objective genetic algorithm is applied to the document clustering in order to complement these disadvantages while its accuracy is analyzed and compared to the existing algorithms. In our experimental results, the multi-objective genetic algorithm introduced in this paper shows the accuracy improvement which is superior to the k-means clustering(about 20 %) and the general genetic algorithm (about 17 %) for the document clustering.

Clustering Amelioration and Optimization with Swarm Intelligence for Color Image Segmentation

Kiranpreet,Prince Verma 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.5

Cluster examination is data mining task for the assignment of collection a set of items in such a path, to the point that questions in the same gathering (called a cluster) are more like one another than to those in different gatherings (clusters). K-means grouping is a technique for group investigation which intends to parcel n perceptions into k groups in which every perception fits in with the cluster with the closest mean. This paper, decided the aftereffect of standard parameter estimations of shading picture division with k-means and the modified k-means with ABC and ACO algorithms. The paper demonstrates that division of color picture with modified k-mean consolidated with swarm Intelligence calculations for color image segmentation gives preferable results over simple k-means and Modified k-means with Ant colony optimization gives better results than modified k-means with Artificial bee colony.