Spectral clustering is a powerful tool for exploratory data analysis. Many existing spectral clustering algorithms typically measure the similarity by...

https://onlinelibrary.wiley.com/doi/pdf/10.4218/etrij.16.0115.0517

Robust Similarity Measure for Spectral Clustering Based on Shared Neighbors

The interest in the analysis and study of clustering techniques have grown since the introduction of new algorithms based on the continuity of the data, where problems related to image segmentation and tracking, amongst others, makes difficult the correct classification of data into their appropriate groups, or clusters. Some new techniques, such as Spectral Clustering (SC), uses graph theory to generate the clusters through the spectrum of the graph created by a similarity function applied to the elements of the database. The approach taken by SC allows to handle the problem of data continuity though the graph representation. Based on this idea, this study uses genetic algorithms to select the groups using the same similarity graph built by the Spectral Clustering method. The main contribution is to create a new algorithm which improves the robustness of the Spectral Clustering algorithm reducing the dependency of the similarity metric parameters that currently affects to the performance of SC approaches. This algorithm, named Genetic Graph-based Clustering (GGC), has been tested with different synthetic and real-world datasets, the experimental results have been compared against classical clustering algorithms like K-Means, EM and SC.

A genetic graph-based clustering algorithm

Community detection in social network is a significant issue in the study of the structure of a network and understanding its characteristics. A community is a significant structure formed by nodes with more connections between them. In recent years, several algorithms have been presented for community detection in social networks among them label propagation algorithm is one of the fastest algorithms, but due to the randomness of the algorithm its performance is not suitable. In this paper, we propose an improved label propagation algorithm called memory-based label propagation algorithm (MLPA) for finding community structure in social networks. In the proposed algorithm, a simple memory element is designed for each node of graph and this element store the most frequent common adoption of labels iteratively. Our experiments on the standard social network datasets show a relative improvement in comparison with other community detection algorithms.

Memory-based label propagation algorithm for community detection in social networks

The island model allows genetic algorithms to effectively maintain diversity through migration between multiple independent populations. Due to its flexibility and modularity, it is commonly employed in distributed and parallel implementations, particularly in recent trends in leveraging the massively parallel cores in GPUs. However, the efficiency and effectiveness of the island model can be considered as its ability to manage its global and local search while minimising the overlap of islands searching in the same area of the solution space. This paper introduces a GPU accelerated island-model genetic algorithm that conducts global search by organising its populations into islands according to the similarity in genotype sequences. Local search is managed through adaptive mechanisms designed to maintain population diversity. The characteristics of the proposed genetic algorithm are investigated with encouraging results demonstrating its robustness and scalability when solving ordered optimisation problems.

/pdf/gpu-accelerated-genetic-algorithm-with-sequence-based-35p57h5tpc.pdf

GPU Accelerated Genetic Algorithm with Sequence-based Clustering for Ordered Problems

In recent years, materialized views (MVs) are widely used to enhance the database performance by storing pre-calculated results of resource-intensive queries in the physical memory. In order to identify which queries may be potentially materialized, database transaction log for a long period of time should be analyzed. The goal of analysis is to distinguish resource-intensive and frequently used queries collected from database log, and optimize these queries by implementation of MVs. In order to achieve greater efficiency of MVs, they were used not only for the optimization of single queries, but also for entire groups of queries that are similar in syntax and execution results. Thus, the problem stated in this article is the development of approach that will allow forming groups of queries with similar syntax around the most resource-intensive queries in order to identify the list of potential candidates for materialization. For solving this problem, we have applied the algorithm of categorical data clustering to the query grouping problem on the step of database log analysis and searching candidates for materialization. In the current work CLOPE algorithm was modified to cover the introduced problem. Statistical and timing indicators were taken into account in order to form the clusters around the most resource intensive queries. Application of modified algorithm CLOPE allowed to decrease calculable complexity of clustering and to enhance the quality of formed groups. ACM CCS (2012) Classification : Information systems→ Data management systems → Database management system engines→Query optimization; Computing methodologies→Cluster analysis *To cite this article: K. Novokhatska and O. Kungurtsev, "Application of Clustering Algorithm CLOPE to the Query Grouping Problem in the Field of Materialized View Maintenance", CIT. Journal of Computing and Information Technology , vol. 24, no. 1, pp. 79-89, 2016.

/pdf/application-of-clustering-algorithm-clope-to-the-query-14c6sz5q0a.pdf

Application of Clustering Algorithm CLOPE to the Query Grouping Problem in the Field of Materialized View Maintenance

As a novel clustering algorithm, spectral clustering is applied in machine learning extensively. Spectral clustering is built upon spectral graph theory, and has the ability to process the clustering of non-convex sample spaces. Most of the existing spectral clustering algorithms are based on k-means algorithm, and k-means algorithm uses the iterative optimization method to find the optimal solution, which is easy to prematurely converge to the local optimal solution. Combined with the global search ability of genetic algorithm, a genetic spectral clustering algorithm is proposed. Compared with the original spectral clustering and k-means clustering analysis based on genetic algorithm, the suggested algorithm reduces the input dimension of the clustering algorithm using dimension reduction of spectral clustering, and replaces the traditional k-means algorithm by genetic k-means algorithm. The experiments show that the suggested algorithm obtains the stable cluster centers, and effectively improves the clustering performance on both artificial data and UCI datasets, which validate the stability and effectiveness of the suggested algorithm.

A Genetic Spectral Clustering Algorithm

As an unsupervised learning method, spectral clustering is widely used in machine learning. Spectral clustering algorithms apply spectral graph theory to solve the clustering of non-convex sphere of sample spaces, so that they can be converged to global optimal solution. After the essence of initialization sensitivity in spectral clustering is analyzed, the prim algorithm is used to select the k initial cluster centers, and a threshold parameter is set to solve the problem that two data objects in the same class are selected as the cluster centers, which leads to get the wrong cluster results and increases the iteration times. Based on above, a heuristic initialization-independent spectral clustering algorithm is proposed. Compared with the traditional spectral clustering algorithm and the initialization-sensitive k-means algorithm, the experiments show that the suggested algorithm not only obtains the stable cluster centers and cluster results, but also effectively decreases the iteration times, accordingly improves the clustering performance, which validates the feasibility and effectiveness of the suggested algorithm.

A Heuristic Initialization-Independent Spectral Clustering

The number of clusters aects the quality of clustering directly. How to automatically determine the number of cluster has become a key issue in the study of spectral clustering. To solve the problem, the articial immune system has been introduced, and in the use of clonal selection and immune response mechanism, we proposed an adaptive spectral clustering algorithm. Primarily, the improved algorithm simulates the primary immune response and secondary immune response in clonal selection algorithm, so we can determine the number of cluster automatically, and obtain the corresponding cluster centers. The results of the experiment show that the algorithm is provided with good stability, it can not only get the right clustering number in the convex datasets, but also can identify non-convex sample data eectively.

An Adaptive Spectral Clustering Algorithm

K-means algorithm is a typical distance-based clustering analysis algorithm, and the neighborhood granular K-means clustering algorithm uses the neighborhood granulation technique on its basis to solve the problem of poor clustering of the K-means clustering algorithm on non-linear separable datasets. However, the use of neighborhood relations in the granulation lead to the inability of the algorithm to deal with outliers effectively, resulting in some samples being misclassified. By introducing the concept of fuzzy set and combining granular computing theory, a new neighborhood granular K-means clustering method is proposed, where samples are constructed using fuzzy neighborhood granularity technique to construct fuzzy neighborhood granules, and the membership between samples is used for neighborhood granularity instead of clear neighborhood relations, which reduces the impact of outliers. A new distance measure for fuzzy neighborhood granules is proposed based on the size and form of the fuzzy neighborhood granules, which reduces the computational complexity. In addition, the algorithm combines the idea of K Nearest Neighbor to define a neighborhood threshold metric that adaptively generates neighborhood thresholds for any data set. It considers the size of the data set objectively and reduces the subjective influence of manual selection of neighborhood threshold. Finally, the Accuracy and Fowlkes Mallows Index values of the algorithm are increased by 6.4 %, 9.21%, 3.5%, and 7.89% on the synthetic data set and UCI data set, respectively, and increased by 8.65% and 9.43% on the seismic facies lithology identification data set, respectively. The experimental results prove the feasibility and effectiveness of the fuzzy neighborhood granular K-means clustering method.

A K-means Clustering Method for Fuzzy Neighborhood Granules

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A Genetic Spectral Clustering Algorithm

A Heuristic Initialization-Independent Spectral Clustering

An Adaptive Spectral Clustering Algorithm

A K-means Clustering Method for Fuzzy Neighborhood Granules