جستجوی مقالات مرتبط با کلیدواژه « خوشه بندی ترکیبی » در نشریات گروه « برق »

An ensemble clustering has been considered as one of the research approaches in data mining, pattern recognition, machine learning and artificial intelligence over the last decade. In clustering, the combination first produces several bases clustering, and then, for their aggregation, a function is used to create a final cluster that is as similar as possible to all the cluster bundles. The input of this function is all base clusters and its output is a clustering called clustering agreement. This function is called an agreement function. Ensemble clustering has been proposed to increase efficiency, strong, reliability and clustering stability. Because of the lack of cluster monitoring, and the inadequacy of general-purpose base clustering algorithms on the other, a new approach called an ensemble clustering has been proposed in which it has been attempted to find an agreed cluster with the highest Consensus and agreement. In fact, ensemble clustering techniques with this slogan, the combination of several poorer models, is better than a strong model. However, this claim is correct if certain conditions (such as the diversity between the members in the consensus and their quality) are met. This article presents an ensemble clustering method. This paper uses the weak clustering method of fuzzy cmeans as a base cluster. Also, by adopting some measures, the diversity of consensus has increased. The proposed hybrid clustering method has the benefits of the clustering algorithm of fuzzy cmeans that has its speed, as well as the major weaknesses of the inability to detect non-spherical and non-uniform clusters. In the experimental results, we have tested the proposed ensemble clustering algorithm with different, up-to-date and robust clustering algorithms on the different data sets. Experimental results indicate the superiority of the proposed ensemble clustering method compared to other clustering algorithms to up-to-date and strong.

Recently, researchers proposed heuristic frameworks which are based on the Wisdom of Crowds in order to evaluate and select the basic results. In these methods, basic results are evaluated by diversity, independency and decentralization metrics. Then, the evaluated results are selected by thresholding, and combined by a consensus function. This paper aims to propose a method for automatic evaluation of the optimized threshold values based on the basic features of the input data in WOCCE. Also, Uniformity, a metric which is based on APMM, is introduced for calculating the diversity of two basic clustering results. Furthermore, Weighted Evidence Accumulation Clustering (WEAC), a new method for considering independency as a weight in the process of combining the basic results, is introduced in this paper. The experimental results indicate that the proposed method has higher efficiency in comparison with the results of other cluster ensemble methods.

Clustering is the process of division of a dataset into subsets that are called clusters, so that objects within a cluster are similar to each other and different from objects of the other clusters. So far, a lot of algorithms in different approaches have been created for the clustering. An effective choice (can combine) two or more of these algorithms for solving the clustering problem. Ensemble clustering combines results of existing clusterings to achieve better performance and higher accuracy. Instead of combining all of existing clusterings, recent decade researchers show, if only a set of clusterings is selected  based on quality and diversity, the result of ensemble clustering would be more accurate. This paper proposes a new method for ensemble clustering based on quality and diversity. For this purpose, firstly first we need a lot of different base clusterings to combine them. Different base clusterings are generated by k-means algorithm with random k in each execution. After the generation of base clusterings, they are put into different groups according to their similarities using a new grouping method. So that clusterings which are similar to each other are put together in one group. In this step, we use normalized mutual information (NMI) or adjusted rand index (ARI) for computing similarities and dissimilarities between the base clustering. Then from each group, a best qualified clustering is selected via a voting based method. In this method, Cluster-validity-indices were used to measure the quality of clustering. So that all members of the group are evaluated by the Cluster-validity-indices. In each group, clustering that optimizes the most number of Cluster-validity-indices is selected.  Finally, consensus functions combine all selected clustering. Consensus function is an algorithm for combining existing clusterings to produce final clusters. In this paper, three consensus functions including CSPA, MCLA, and HGPA have used for combining clustering. To evaluate proposed method, real datasets from UCI repository have used. In experiment section, the proposed method is compared with the well-known and powerful existing methods. Experimental results demonstrate that proposed algorithm has better performance and higher accuracy than previous works.

Clustering is one of the most important field of data mining that aims to divide data into meaningful subsets which are called clusters. This technique involves the process of finding natural groupings in the data set based on the similarities and di similarities which a little or no information about data are available. Over the decades, many clustering algorithms are created in different approaches or a combination of them. In this paper, an algorithm based on density and hierarchical approaches is presented. DBSCAN is one of the algorithms presented in the density-based approach. This algorithm requires two parameters that its determination is a great challenge. In the proposed method, DBSCAN algorithm parameters can be set without user involvement, so that potential clusters are found automatically. The clusters which are so close to each other are merged together until the quality of the final clusters to be enhanced properly. Thus, clusters could be more accurate and high quality. Finally, in order to test the new proposed algorithm, the real dataset in the UCI machine learning repository was used. The results indicate that the new algorithm is more efficient and accurate, and its speed is better than previous methods.

Most of the recent studies have tried to create diversity in primary results and then applied a consensus function over all the obtained results to combine the weak partitions. In this paper a clustering ensemble method is proposed which is based on a subset of primary clusters. The main idea behind this method is using more stable clusters in the ensemble. The stability is applied as a goodness measure of the clusters. The clusters which satisfy a threshold of this measure are selected to participate in the ensemble. For combining the chosen clusters¡ a co-association based consensus function is applied. A new EAC based method which is called Extended Evidence Accumulation Clustering¡ EEAC¡ is proposed for constructing the Co-association Matrix from the subset of clusters. The proposed method is evaluated on five different UCI repository data sets. The empirical studies show the significant improvement of the proposed method in comparison with other ones.

One of the great challenges in Social Network Analysis (SNA) is community detection. Community is a group of vertices which have high intra connections and sparse inter connections. Community detection or Clustering reveals community structure of social networks and hidden relationships among their constituents. By considering the increase of datasets related to social networks, we need scalable algorithms to analyze these networks. Most of the community detection methods currently available are not deterministic and their results typically depend on specific random seeds or initial conditions. Ensemble clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. In this paper we propose an approach which goals in finding robust communities, with using ensemble community detection and we show that the proposed approach can be combined with any existing method in a self-consistent way, enhancing considerably the performance of the resulting partitions. The results of this study can be used for many issues such as more accurately identify community detection, marketing, advertising, networking and search engine optimization.

Most of the recent studies have tried to create diversity in primary results and then applied a consensus function over all the obtained results to combine the weak partitions. In this paper a clustering ensemble method is proposed which is based on a subset of primary clusters. The main idea behind this method is using more stable clusters in the ensemble. The stability is applied as a goodness measure of the clusters. The clusters which satisfy a threshold of this measure are selected to participate in the ensemble. For combining the chosen clusters, a co-association based consensus function is applied. A new EAC based method which is called Extended Evidence Accumulation Clustering, EEAC, is proposed for constructing the Co-association Matrix from the subset of clusters. The proposed method is evaluated on five different UCI repository data sets. The empirical studies show the significant improvement of the proposed method in comparison with other ones.