Rough Clustering Algorithm Research Articles

Objective-Based Rough c-Means Clustering

Conventional clustering algorithms classify a set of objects into some clusters with clear boundaries, that is, one object must belong to one cluster. However, many objects belong to more than one cluster in real world since the boundaries of clusters generally overlap with each other. Fuzzy-set representation of clusters makes it possible for each object to belong to more than one cluster. On the other hand, the fuzzy degree is sometimes regarded as too descriptive for interpreting clustering results. Rough-set representation could deal with such cases. Clustering based on rough set could provide a solution that is less restrictive than conventional clustering and less descriptive than fuzzy clustering. This paper proposes a rough clustering algorithm which is based on optimization of an objective function and the calculation formula of cluster centers is the same as one by Lingras et al. Moreover, it shows effectiveness of our proposed clustering algorithm in comparison with other algorithms.

Rough Clustering Method Based on Particle Swarm Optimization Algorithm

As a relatively novel clustering approach, Particle Swarm Optimization (PSO) prevents k-means algorithm from falling into local optimum effectively, and has made relatively notable successes in clustering, however, using Hard C-Means algorithm when randomly obtaining initial clustering centers is required in most existing PSOs, while no definite limit existing in these samples actually. Based on this, we utilized an improved PSO; along with effective processing methods on boundary objects of Rough Set Theory, we proposed a new rough clustering algorithm based on PSO. It can adjust the upper and lower approximations weighting factors dynamically, and coordinate the proportions of upper and lower approximations in different generations as well. Finally, we compared it with several common clustering methods using Iris dataset of UCI. It turned out that the algorithm has higher accuracy and stability, along with better comprehensive performance.

Research in Social Network Based on Rough Set Clustering Algorithm

This paper uses an improved algorithm based on rough set and K-means clustering to do research in social network, and it solves the problem of vague border. Social networks’ community is similar to the cluster analysis in data mining. However ,when the K-means clustering algorithm is used in social network,there are several disadvantages as following: how to determine the value of K,and the relations among community node and the community. This paper solves the disadvantages using rough set clustering ideas. This method is mainly used to find overlapping communities, and it can multi-anglely reflect the social network information better. The algorithm is applied to experiment data, and results show that this method improves the accuracy of community division obviously compared with other algorithms.

Dynamic rough clustering and its applications

Dynamic data mining has gained increasing attention in the last decade. It addresses changing data structures which can be observed in many real-life applications, e.g. buying behavior of customers. As opposed to classical, i.e. static data mining where the challenge is to discover pattern inherent in given data sets, in dynamic data mining the challenge is to understand – and in some cases even predict – how such pattern will change over time. Since changes in general lead to uncertainty, the appropriate approaches for uncertainty modeling are needed in order to capture, model, and predict the respective phenomena considered in dynamic environments. As a consequence, the combination of dynamic data mining and soft computing is a very promising research area. The proposed algorithm consists of a dynamic clustering cycle when the data set will be refreshed from time to time. Within this cycle criteria check if the newly arrived data have structurally changed in comparison to the data already analyzed. If yes, appropriate actions are triggered, in particular an update of the initial settings of the cluster algorithm. As we will show, rough clustering offers strong tools to detect such changing data structures. To evaluate the proposed dynamic rough clustering algorithm it has been applied to synthetic as well as to real-world data sets where it provides new insights regarding the underlying dynamic phenomena.

Overlapping-Based Rough Clustering Algorithm

摘要: 传统的聚类算法大都得到了样本集的一个划分，类之间是严格的互斥关系，而现实世界中类与类之间往往没有明确的边界。该文将粗糙集理论引入到聚类分析中，提出了一种基于覆盖的粗糙聚类算法KMMRSC，它用多个中心点代表一个类，并用上、下近似来刻画样本的归属，类与类之间是一种覆盖关系。实验结果表明，该算法聚类质量优于k-均值算法，且能发现非球状簇。 关键词: 粗糙聚类; 覆盖; 多中心点

Experimental Comparison of Iterative Versus Evolutionary Crisp and Rough Clustering

Researchers have proposed several Genetic Algorithm (GA) based crisp clustering algorithms. Rough clustering based on Genetic Algorithms, Kohonen Self-Organizing Maps, K-means algorithm are also reported in literature. Recently, researchers have combined GAs with iterative rough clustering algorithms such as K-means and K-Medoids. Use of GAs makes it possible to specify explicit optimization of cluster validity measures. However, it can result in additional computing time. In this paper we compare results obtained using K-means, GA K-means, rough K-means, GA rough K-means and GA rough K-medoid algorithms. We experimented with a synthetic data set, a real world data set, and a standard dataset using a total within cluster variation, average precision, and execution time required as the criteria for comparison.

Experimental Comparison of Iterative Versus Evolutionary Crisp and Rough Clustering

Researchers have proposed several Genetic Algorithm (GA) based crisp clustering algorithms. Rough clustering based on Genetic Algorithms, Kohonen Self-Organizing Maps, K-means algorithm are also reported in literature. Recently, researchers have combined GAs with iterative rough clustering algorithms such as K-means and K-Medoids. Use of GAs makes it possible to specify explicit optimization of cluster validity measures. However, it can result in additional computing time. In this paper we compare results obtained using K-means, GA K-means, rough K-means, GA rough K-means and GA rough K-medoid algorithms. We experimented with a synthetic data set, a real world data set, and a standard dataset using a total within cluster variation, average precision, and execution time required as the criteria for comparison.

An Approach for Construction of Fault Trees Using Rough Set and Fuzzy Clustering Algorithm

This paper presents a novel methodology for the construction of fault tree using rough set and clustering algorithm. Taking advantage of the strong ability of RS theory in processing large data and eliminating redundant information, this method can remove irrelevant factors from the original fault data and reduce the dimension of processing data which helps to overcome fuzzy clustering algorithm's defect when process large database. The method is introduced and explained in details and its correctness and completeness is validated by Fault tree construction of oil-immersed traction transformers.

Adaptive Rough Entropy Clustering Algorithms in Image Segmentation

High quality performance of image segmentation methods presents one leading priority in design and implementation of image analysis systems. Incorporating the most important image data information ...

Adaptive Rough Entropy Clustering Algorithms in Image Segmentation

High quality performance of image segmentation methods presents one leading priority in design and implementation of image analysis systems. Incorporating the most important image data information into segmentation process has resulted in development of innovative frameworks such as fuzzy systems, rough systems and recently rough - fuzzy systems. Data analysis based on rough and fuzzy systems is designed to apprehend internal data structure in case of incomplete or uncertain information. Rough entropy framework proposed in [12,13] has been dedicated for application in clustering systems, especially for image segmentation systems. We extend that framework into eight distinct rough entropy measures and related clustering algorithms. The introduced solutions are capable of adaptive incorporation of the most important factors that contribute to the relation between data objects and makes possible better understanding of the image structure. In order to prove the relevance of the proposed rough entropy measures, the evaluation of rough entropy segmentations based on the comparison with human segmentations from Berkeley and Weizmann image databases has been presented. At the same time, rough entropy based measures applied in the domain of image segmentation quality evaluation have been compared with standard image segmentation indices. Additionally, rough entropy measures seem to comprehend properly properties validated by different image segmentation quality indices.

Rough Clustering of Destination Image Data Using an Evolutionary Algorithm

This article describes a hybrid computational intelligence technique, an evolutionary algorithm based rough clustering algorithm. The technique of cluster analysis is fundamental in traditional data analysis and data mining, and is used to group similar objects together. Many clustering methods have been identified, including the commonly used k-means approach, which is dependent on initial starting points and requires the number of clusters to be specified in advance. The rough clustering algorithm described in this article is able to overcome these limitations. Rough clusters are defined in a similar manner to the rough set concept developed by Pawlak—that is, using a lower and upper approximation. This allows for multiple cluster membership for objects in the data set. The lower approximation of a rough cluster contains objects that only belong to that cluster. The upper approximation of a rough cluster contains objects that belong to more than one cluster. The article describes the template, the data structure used to describe rough clusters. It also provides an overview of the evolutionary algorithm used to develop viable cluster solutions, consisting of an optimal number of templates, which provide easily interpreted descriptions of the clusters. This evolutionary algorithm based rough clustering algorithm was tested on a large data set of perceptions of city destination image attributes. Some preliminary results are presented.