Evolutionary Clustering Approach Research Articles

An adaptive evolutionary multi-objective clustering based on the data properties of the base partitions

Evolutionary multi-objective clustering (EMOC) is a modern clustering technique in which the general concepts of evolutionary multi-objective optimization are applied to the clustering problem. The design and definition of clustering are difficult problems, and the choice of the objective functions and parameter setting of the algorithms are still challenges. In this paper, we propose the adaptive evolutionary multi-objective clustering approach based on data properties (AEMOC). AEMOC considers a new metric to evaluate the base partitions (candidate solutions used in the initial population) generated by minimum spanning tree clustering. This metric is applied to: (1) determine properties of the data, such as separation and overlap; and (2) define an offline selection of objective functions and parameter settings of the multi-objective algorithm. The information regarding the data properties allows AEMOC to avoid unnecessary data processing in datasets in which the initialization provides optimal solutions and optimization is not required. AEMOC presented promising results considering a diverse set of artificial and real-life datasets, based on two aspects: (1) it succeeded in the determination of the data properties of the base partitions and verifying the potential clustering quality, presenting a correlation of 0.8 with a reference metric, and (2) it provided better clustering results than reference EMOC approaches, achieving a general gain in the clustering performance of 3% in real-life datasets and 7% in artificial datasets.

An analysis of the admissibility of the objective functions applied in evolutionary multi-objective clustering

A variety of clustering criteria have been applied as an objective function in Evolutionary Multi-Objective Clustering approaches (EMOCs). However, most EMOCs do not provide detailed analysis regarding the choice and usage of the objective functions. In general, the choice of the objective functions only considers the desired clustering properties, and most EMOCs present in the literature do not consider aspects of multi-objective optimization, such as the search direction, in their design. Aiming to support a better choice and definition of the objectives in the EMOCs, this paper proposes an analysis of the clustering criteria admissibility to examine the search direction and evaluate their potential in finding optimal results. We consider the fundamentals of the evaluation of a heuristic function to analyze the clustering criteria and demonstrate how they can influence the optimization. As a result, this study provides a detailed analysis of the main objective functions found in the literature and evaluates how the initialization interferes with their admissibility. Also, we highlight some common practices and issues found in some established EMOCs. Furthermore, we provide insights regarding how to combine and use the clustering criteria in the EMOCs.

Fuzzy decision trees embedded with evolutionary fuzzy clustering for locating users using wireless signal strength in an indoor environment

Location estimation is one of the critical requirement for developing smart environment products. Due to huge utilization and accessibility of WiFi infrastructure facility in indoor environments, researchers widely studied this technology to locate users accurately to provide several services instantly. In this research work, a hybrid algorithm namely fuzzy decision tree (FDT) with evolutionary fuzzy clustering methods is adopted for optimal user localization in a closed environment. Here we consider the wireless signal strengths received from the smart phones as predictors and the location of the user as the classification label. The required data for the current research is collected from the physical facility available at an office location in USA. The classification results obtained are promising enough to show that the evolutionary clustering approaches provide good fuzzy clusters for FDT induction with better accuracy.

A graph-based clustering algorithm for software systems modularization

Clustering algorithms , as a modularization technique, are used to modularize a program aiming to understand large software systems as well as software refactoring. These algorithms partition the source code of the software system into smaller and easy-to-manage modules (clusters). The resulting decomposition is called the software system structure (or software architecture). Due to the NP-hardness of the modularization problem, evolutionary clustering approaches such as the genetic algorithm have been used to solve this problem. These methods do not make much use of the information and knowledge available in the artifact dependency graph which is extracted from the source code. To overcome the limitations of the existing modularization techniques, this paper presents a new modularization technique named GMA (Graph-based Modularization Algorithm). In this paper, a new graph-based clustering algorithm is presented for software modularization. To this end, the depth of relationships is used to compute the similarity between artifacts, as well as seven new criteria are proposed to evaluate the quality of a modularization. The similarity presented in this paper enables the algorithm to use graph-theoretic information. To demonstrate the applicability of the proposed algorithm, ten folders of Mozilla Firefox with different domains and functions, along with four other applications, are selected. The experimental results demonstrate that the proposed algorithm produces modularization closer to the human expert’s decomposition (i.e., directory structure) than the other existing algorithms. The proposed algorithm is expected to help a software designer in the software reverse engineering process to extract easy-to-manage and understandable modules from source code.

Automatic classification of nuclear physics data via a Constrained Evolutionary Clustering approach

This paper presents an automatic method for data classification in nuclear physics experiments based on evolutionary computing and vector quantization. The major novelties of our approach are the fully automatic mechanism and the use of analytical models to provide physics constraints, yielding to a fast and physically reliable classification with nearly-zero human supervision. Our method is successfully validated using experimental data produced by stacks of semiconducting detectors. The resulting classification is highly satisfactory for all explored cases and is particularly robust to noise. The algorithm is suitable to be integrated in the online and offline analysis software of existing large complexity detection arrays for the study of nucleus–nucleus collisions at low and intermediate energies.

Detecting and Tracking Community Structure in Temporal Networks: A Low-Rank + Sparse Estimation Based Evolutionary Clustering Approach

Networks provide a powerful tool to model complex systems where the different entities in the system are presented by nodes and their interactions by edges. With the availability of network-type data, different community detection algorithms have been proposed to investigate the organization of the nodes within these networks. In particular, numerous graph-based community detection algorithms have been developed for static networks. However, most real complex systems vary with time. Consequently, it is important to develop graph-based community detection techniques for temporal networks. In this paper, a new low-rank + sparse estimation based evolutionary spectral clustering approach is proposed to detect and track the community structure in temporal networks. The proposed method decomposes the network into low-rank and sparse parts and obtains smooth cluster assignments by minimizing the subspace distance between consecutive time points. The extracted low-rank adjacency matrix is then used for clustering and the subspaces are defined through spectral embedding. The introduced framework is robust to noise and outliers and can detect the community structure in both binary and weighted temporal networks efficiently without making any prior assumptions about the network structure. The proposed approach is evaluated on several weighted and binary simulated and real temporal networks. The results show that the proposed algorithm can detect and track the correct community structure over time efficiently and outperforms state-of-the-art algorithms.

An Evolutionary Feature Clustering Approach for Anomaly Detection Using Improved Fuzzy Membership Function

Traditionally, IDS have been developed by applying machine learning techniques and followed single learning mechanisms or multiple learning mechanisms. Dimensionality is an important concern which affects classification accuracies and eventually the classifier performance. Feature selection approaches are widely studied and applied in research literature. In this work, a new fuzzy membership function to detect anomalies and intrusions and a method for dimensionality reduction is proposed. CANN could not address R2L and U2R attacks and have completely failed by showing these attack accuracies almost zero. Following CANN, the CLAPP approach has shown better classifier accuracies when compared to classifiers kNN, and SVM. This research aims at improving the accuracy achieved by CLAPP, CANN, and kNN. Experimental results show accuracies obtained using proposed approach is better when compared to other existing approaches. In particular, the detection of U2R and R2L attacks to user accuracies are recorded to be very much promising.

Transfer learning-assisted multi-objective evolutionary clustering framework with decomposition for high-dimensional data

Although multi-objective evolutionary subspace clustering approaches have shown promise in handling high-dimensional datasets, their performance is restricted by two main drawbacks. First, their local search strategies have not been well investigated. Second, while exploring the search space, they neglect the useful knowledge from previously solved problems. To tackle these issues, this paper proposes a transfer learning-assisted multi-objective evolutionary clustering framework with decomposition. Firstly, we provide a decomposition-based local search strategy. To capture a comprehensive data structure, this strategy updates the weights of features by considering both the within-class compactness and between-class separation, and spontaneously balances the two properties. Secondly, we develop a knowledge transfer strategy. By transferring search experience from a previously solved clustering problem, the strategy improves the search efficiency, consequently enhances the clustering accuracy of the current problem. It has a closed-form solution and can transfer knowledge across both homogeneous and heterogeneous problems from either different or the same domains. Finally, we conduct an extensive experimental study on the framework by comparing with six representative subspace clustering approaches on a wide range of benchmarks and real-world applications. Results demonstrate the superiority of our framework.

A Generalized Enhanced Quantum Fuzzy Approach for Efficient Data Clustering

Data clustering is a challenging task to gain insights into data in various fields. In this paper, an Enhanced Quantum-Inspired Evolutionary Fuzzy C-Means (EQIE-FCM) algorithm is proposed for data clustering. In the EQIE-FCM, quantum computing concept is utilized in combination with the FCM algorithm to improve the clustering process by evolving the clustering parameters. The improvement in the clustering process leads to improvement in the quality of clustering results. To validate the quality of clustering results achieved by the proposed EQIE-FCM approach, its performance is compared with the other quantum-based fuzzy clustering approaches and also with other evolutionary clustering approaches. To evaluate the performance of these approaches, extensive experiments are being carried out on various benchmark datasets and on the protein database that comprises of four superfamilies. The results indicate that the proposed EQIE-FCM approach finds the optimal value of fitness function and the fuzzifier parameter for the reported datasets. In addition to this, the proposed EQIE-FCM approach also finds the optimal number of clusters and more accurate location of initial cluster centers for these benchmark datasets. Thus, it can be regarded as a more efficient approach for data clustering.

An evolutionary clustering approach based on temporal aspects for context-aware service recommendation

Over the last years, recommendation techniques have emerged to cope with the challenging task of optimal service selection, and to help consumers satisfy their needs and preferences. However, most existing models on service recommendation only consider the traditional user-service relation, while in the real world, the perception and popularity of Web services may depend on several conditions including temporal, spatial and social constraints. Such additional factors in recommender systems influence users’ preferences to a large extent. In this paper, we propose a context-aware Web service recommendation approach with a specific focus on time dimension. First, K-means clustering method is hybridized with a multi-population variant of the well-known Particle Swarm Optimization (PSO) in order to exclude the less similar users which share few common Web services with the active user in specific contexts. Slope One method is, then, applied to predict the missing ratings in the current context of user. Finally, a recommendation algorithm is proposed in order to return the top-rated services. Experimental studies confirmed the accuracy of our recommendation approach when compared to three existing solutions.

Dynamic evolutionary clustering approach based on time weight and latent attributes for collaborative filtering recommendation

Collaborative filtering is one of the most widely used individual recommendation algorithms. The traditional collaborative filtering recommendation algorithm takes less care of time variation, which may be inaccurate in real surroundings. A novel dynamic evolutionary clustering algorithm based on time weight and latent attributes is proposed. According to the time effect of historical information in recommendation system, forgetting curve is introduced to better grasp the recent interest of the users. To gather users with similar interest into the same cluster, item characteristics and user attributes are mined. Therefore, network model is established by introducing the forgetting function to score matrix, utilizing item characteristics and user attributes. Items and users are regarded as heterogenous nodes in network. Furthermore, a novel dynamic evolutionary clustering algorithm is adopted to divide users and items set into K clusters, and individuals with higher similarity are clustered. The preferences of users in the same cluster are similar. Then, collaborative filtering is applied in each cluster to predict the ratings. Finally, the target users are recommended predicted according to prediction ratings. Simulations show that the presented method gains better recommendation accuracy in comparison of existing algorithms through MovieLens100k, Restaurant & consumer and CiaoDVD data sets.

An Evolutionary Intuitionistic Fuzzy K-means Clustering Approach based Cluster Head Selection in MANET

An Evolutionary Intuitionistic Fuzzy K-means Clustering Approach based Cluster Head Selection in MANET

Enhanced evolutionary heuristic approaches for remote metering smart grid networks

For a smart grid network, the communication infrastructure is an important part to provide the stability and reliability. To build an efficient communication network is crucial for the success of a smart grid network. This study improves the previous published work by proposing two novel evolutionary heuristic approaches (discrete differential evolutionary clustering approach and particle swarm optimisation clustering approach) to obtain the minimum number and location of the powerful nodes to fulfil the connectivity requirement of a smart meters network. The contribution of this study includes the designing and implementing of the two novel evolutionary heuristic approaches which could significantly improve the optimal solution [the least number of the local data centres (powerful nodes)] for a smart meter network which has arbitrary number of smart meters (ordinary nodes) with arbitrary transmission range. Comprehensive case studies and numerical statistical analysis demonstrate that the approaches could efficiently obtain better optimal results, and the connectivity of the smart meter network could be fulfilled with minimum number of the powerful nodes.

Evolutionary Clustering for Mining and Tracking Dynamic Multilayer Networks

This article proposes a framework for community discovery in temporal multiplex networks by extending the evolutionary clustering approach to encompass both time and multiple dimensions. In this extended framework, the problem of finding community structures for time‐evolving networks with multiple types of ties is reformulated by adding the concept of dimensional smoothness, relative to a single timestamp, to that of temporal smoothness, at the base of evolutionary clustering. At each timestamp, the method tries to maximize the quality of the clustering obtained for the current multidimensional network and to minimize the differences with respect to that obtained at the previous timestamp. Moreover, the evolution of a community between two consecutive timestamps is maintained by exploiting the Hungarian approach, which determines the best cluster correspondence between two consecutive timestamps. Experiments on synthetic and real‐world networks show the capability of the approach in discovering and tracking group organization of actors constituting the network.

An Evolutionary Clustering Approach with FCM in Layering Scheme for Computer Security in Network

An Evolutionary Clustering Approach with FCM in Layering Scheme for Computer Security in Network

Label propagation based evolutionary clustering for detecting overlapping and non-overlapping communities in dynamic networks

Since real-world networks evolve over time, detecting communities in dynamic networks is a challenging research problem with wide applications. In this paper, we first improve our previous method and propose a more stable algorithm which is label-propagation-based for the discovery of communities in complex networks. Then, we present a novel evolutionary clustering approach DLPAE for dynamic networks based on the stable algorithm. According to DLPAE, community labels of nodes are determined by their neighbors, and a confidence (i.e., the importance of its neighbor to the node) is attached to each neighbor. During clustering, the confidences of nodes are calculated in terms of the structures of the current network and the network at last timestamp. We compute confidences’ variance of each node and update nodes’ labels in a descending order according to the values. In our setting, each node can keep one or more labels with belonging coefficients no less than a threshold, which renders DLPAE suitable for detecting overlapping and non-overlapping communities in dynamic networks. Experimental results on both real and synthetic datasets show the ability of DLPAE to detect overlapping and non-overlapping communities in dynamic networks, and demonstrate its higher accuracy compared to other related methods.

An evolutionary restricted neighborhood search clustering approach for PPI networks

Protein–protein interaction networks have been broadly studied in the last few years, in order to understand the behavior of proteins inside the cell. Proteins interacting with each other often share common biological functions or they participate in the same biological process. Thus, discovering protein complexes made of a group of proteins strictly related can be useful to predict protein functions. Clustering techniques have been widely employed to detect significant biological complexes. In this paper, we integrate one of the most popular network clustering techniques, namely the Restricted Neighborhood Search Clustering (RNSC), with evolutionary computation. The two cost functions introduced by RNSC, besides a new one that combines them, are used by a Genetic Algorithm as fitness functions to be optimized. Experimental evaluations performed on two different groups of interactions of the budding yeast Saccharomyces cerevisiae show that the clusters obtained by the genetic approach are a larger number of those found by RNSC, though this method predicts more true complexes.

2561 多目的設計解の進化的クラスタリングアプローチとその可視化

In recent years, researchers have become increasingly interested in multi-objective optimization (MOO). However, little work has been done to interpret the MOO solutions in multidimensional space. This paper presents an evolutionary clustering approach to interpret the solutions with its visualization. To comprehend the correlation of each solution and each cluster, the MOO solutions were visualized into the principal component space and the real design space. We recommend that the approach outlined in this study would be applicable to the MOO solutions in the space beyond human perception.