Membrane Clustering: A Novel Clustering Algorithm under Membrane Computing

Membrane computing (known as P systems) is a class of distributed parallel computing models, this paper presents a novel algorithm under membrane computing for solving the data clustering problem, called as membrane clustering algorithm. The clustering algorithm is based on a tissue-like P system with a loop structure of cells. The objects of the cells express the candidate cluster centers and are evolved by the evolution rules. Based on the loop membrane structure, the communication rules realize a local neighborhood topology, which helps the co-evolution of the objects and improves the diversity of objects in the system. The tissue-like P system can effectively search for the optimal clustering partition with the help of its parallel computing advantage. The proposed clustering algorithm is evaluated on four artificial data sets and six real-life data sets. Experimental results show that the proposed clustering algorithm is superior or competitive to classical k-means algorithm and several evolutionary clustering algorithms recently reported in the literature

Community Detection Using Revised Medoid-Shift Based on KNN

Community detection becomes an important problem with the booming of social networks. As an excellent clustering algorithm, Mean-Shift can not be applied directly to community detection, since Mean-Shift can only handle data with coordinates, while the data in the community detection problem is mostly represented by a graph that can be treated as data with a distance matrix (or similarity matrix). Fortunately, a new clustering algorithm called Medoid-Shift is proposed. The Medoid-Shift algorithm preserves the benefits of Mean-Shift and can be applied to problems based on distance matrix, such as community detection. One drawback of the Medoid-Shift algorithm is that there may be no data points within the neighborhood region defined by a distance parameter. To deal with the community detection problem better, a new algorithm called Revised Medoid-Shift (RMS) in this work is thus proposed. During the process of finding the next medoid, the RMS algorithm is based on a neighborhood defined by KNN, while the original Medoid-Shift is based on a neighborhood defined by a distance parameter. Since the neighborhood defined by KNN is more stable than the one defined by the distance parameter in terms of the number of data points within the neighborhood, the RMS algorithm may converge more smoothly. In the RMS method, each of the data points is shifted towards a medoid within the neighborhood defined by KNN. After the iterative process of shifting, each of the data point converges into a cluster center, and the data points converging into the same center are grouped into the same cluster

Measuring Spatial Dynamics in Metropolitan Areas

This paper introduces a new approach to measuring neighborhood change. Instead of the traditional method of identifying “neighborhoods†a priori and then studying how resident attributes change over time, our approach looks at the neighborhood more intrinsically as a unit that has both a geographic footprint and a socioeconomic composition. Therefore, change is identified when both as- pects of a neighborhood transform from one period to the next. Our approach is based on a spatial clustering algorithm that identifies neighborhoods at two points in time for one city. We also develop indicators of spatial change at both the macro (city) level as well as local (neighborhood) scale. We illustrate these methods in an application to an extensive database of time-consistent census tracts for 359 of the largest metropolitan areas in the US for the period 1990-2000.

A Novel Clustering Algorithm Inspired by Membrane Computing

P systems are a class of distributed parallel computing models; this paper presents a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. The objects of the cells express the candidate centers of clusters and are evolved by the evolution rules. Based on the loop membrane structure, the communication rules realize a local neighborhood topology, which helps the coevolution of the objects and improves the diversity of objects in the system. The tissue-like P system can effectively search for the optimal partitioning with the help of its parallel computing advantage. The proposed clustering algorithm is evaluated on four artificial data sets and six real-life data sets. Experimental results show that the proposed clustering algorithm is superior or competitive to k-means algorithm and several evolutionary clustering algorithms recently reported in the literature

Improved Firefly Algorithm with Variable Neighborhood Search for Data Clustering

من بين الخوارزميات الأدلة العليا (الميتاهيورستك)، تعد الخوارزميات القائمة على البحوث المتعددة (المجتمع) خوارزمية بحث استكشافية متفوقة كخوارزمية البحث المحلية من حيث استكشاف مساحة البحث للعثور على الحلول المثلى العالمية. ومع ذلك، فإن الجانب السلبي الأساسي للخوارزميات القائمة على البحوث المتعددة (المجتمع) هو قدرتها الاستغلالية المنخفضة، مما يمنع توسع منطقة البحث عن الحلول المثلى. خوارزمية اليَرَاعَة المضيئة (Firefly (FA هي خوارزمية تعتمد على المجتمع والتي تم استخدامها على نطاق واسع في مشاكل التجميع. ومع ذلك، فإن FA مقيد بتقاربها السابق لأوانه عندما لا يتم استخدام استراتيجيات بحث محلي لتحسين جودة حلول المجموعات في منطقة المجاورة واستكشاف المناطق العالمية في مساحة البحث. على هذا الأساس، فإن الهدف من هذا العمل هو تحسين FA باستخدام البحث المتغير في الأحياء (VNS) كطريقة بحث محلية (FA-VNS)، وبالتالي توفير فائدة VNS للمفاضلة بين قدرات الاستكشاف والاستغلال. يسمح FA-VNS المقترح لليراعات بتحسين حلول التجميع مع القدرة على تعزيز حلول التجميع والحفاظ على تنوع حلول التجميع أثناء عملية البحث باستخدام مشغلي الاضطراب في VNS. لتقييم أداء الخوارزمية، يتم استخدام ثماني مجموعات بيانات معيارية مع أربع خوارزميات تجميع معروفة. تشير المقارنة وفقًا لمقاييس التقييم الداخلية والخارجية إلى أن FA-VNS المقترحة يمكن أن تنتج حلول تجميع أكثر إحكاما من خوارزميات التجميع المعروفة.Among the metaheuristic algorithms, population-based algorithms are an explorative search algorithm superior to the local search algorithm in terms of exploring the search space to find globally optimal solutions. However, the primary downside of such algorithms is their low exploitative capability, which prevents the expansion of the search space neighborhood for more optimal solutions. The firefly algorithm (FA) is a population-based algorithm that has been widely used in clustering problems. However, FA is limited in terms of its premature convergence when no neighborhood search strategies are employed to improve the quality of clustering solutions in the neighborhood region and exploring the global regions in the search space. On these bases, this work aims to improve FA using variable neighborhood search (VNS) as a local search method, providing VNS the benefit of the trade-off between the exploration and exploitation abilities. The proposed FA-VNS allows fireflies to improve the clustering solutions with the ability to enhance the clustering solutions and maintain the diversity of the clustering solutions during the search process using the perturbation operators of VNS. To evaluate the performance of the algorithm, eight benchmark datasets are utilized with four well-known clustering algorithms. The comparison according to the internal and external evaluation metrics indicates that the proposed FA-VNS can produce more compact clustering solutions than the well-known clustering algorithms

A New Approach of Dynamic Clustering Based on Particle Swarm Optimization and Application in Image Segmentation

This paper presents a new approach of dynamic clustering based on improved Particle Swarm Optimization (PSO) and which is applied to image segmentation (called DCPSONS). Firstly, the original PSO algorithm is improved by using diversity mechanism and neighborhood search strategy. The improved PSO is then combined with the well-known data clustering k-means algorithm for dynamic clustering problem where the number of clusters has not yet been known. Finally, DCPSONS is applied to image segmentation problem, in which the number of clusters is automatically determined. Experimental results in using sixteen benchmark data sets and several images of synthetic and natural benchmark data demonstrate that the proposed DCPSONS algorithm substantially outperforms other competitive algorithms in terms of accuracy and convergence rate

Random Graph Generator for Bipartite Networks Modeling

The purpose of this article is to introduce a new iterative algorithm with properties resembling real life bipartite graphs. The algorithm enables us to generate wide range of random bigraphs, which features are determined by a set of parameters.We adapt the advances of last decade in unipartite complex networks modeling to the bigraph setting. This data structure can be observed in several situations. However, only a few datasets are freely available to test the algorithms (e.g. community detection, influential nodes identification, information retrieval) which operate on such data. Therefore, artificial datasets are needed to enhance development and testing of the algorithms. We are particularly interested in applying the generator to the analysis of recommender systems. Therefore, we focus on two characteristics that, besides simple statistics, are in our opinion responsible for the performance of neighborhood based collaborative filtering algorithms. The features are node degree distribution and local clustering coeficient