Ant-based Clustering Algorithm Research Articles

A Hybrid Clustered Ant Colony Optimization Approach for the Hierarchical Multi-Switch Multi-Echelon Vehicle Routing Problem with Service Times

In this paper, the Hierarchical Multi Switch Multi Echelon Vehicle Routing Problem with Service Times (HMSME-VRP-ST) is presented. This novel problem considers distribution applications in which goods are delivered either directly from a central depot or through intermediate facilities called switch points. Commodities are loaded into interchangeable containers called swap bodies at the central depot, and can be transferred from one vehicle to another at the switch points. The goal is to minimize fixed and variable costs of the vehicle fleet and swap-bodies while serving a predetermined set of customers. The routes are constrained by time and swap bodies by loading capacity. A mathematical model of the HMSME-VRP-ST is presented, and a Hybrid Clustered Ant Colony Optimization (HCACO) algorithm is proposed to address the complexity of the problem. The approach utilizes the ant-based clustering algorithm, combining the density and connectivity properties of clustering for creating promising neighborhoods with the solution construction methodology and pheromone-based memory of the Ant Colony Optimization framework. Additionally, two local search schemes based on Variable Neighborhood Descent are incorporated to further improve the generated solutions. The behavior of each HCACO variant is analyzed, and their results are compared to a Greedy Randomized Adaptive Search Procedure metaheuristic in 36 newly generated benchmarks comprising of clustered, uniformly random, and mixed clustered-random instances.

The temporal inference with the use of ant-based clustering algorithm and flow graphs in the problem of prognosing complications of medical surgical procedures

In the era of a rapidly aging European society, the demand for proven clinical decision support systems, links health observations with medical knowledge in order to assist clinicians in decision making is constantly growing. An increasing problem for this type of systems is not only the size of the processed data sets but also the heterogeneity of these data. Clinical forecasting often requires processing of both numerical data and multi-category data which are temporal. The conducted research has shown that a good solution to this problem may lie in the use of temporal inference, the ant-based clustering algorithm, rough sets, and fuzzy sets. The experiments used a real set of medical data representing cases of a disease that significantly reduces a woman's quality of life. Each case of uterine myoma disease (which affects more than 50% of women over the age of 35) is represented by more than 140 heterogeneous features. An incorrect decision about the type of surgery (thermoablation or surgery) not only affects female fertility but also the high risk of complications. Therefore, the solution discussed in this paper may turn out to be extremely important.

Location-Based Mobile Community Using Ants-Based Cluster Algorithm

A location based service (LBS) is widely used on modern smartphone around the world asits built-in features. Each smartphone can access a google API or map. People can therefore share their location (latitude and longitude) among friends. Many LBS spots can easily form “location based mobile community (LBMC).” Since the nodes are mobile, the community group changes dynamically and is unstructured. Ant-based clustering algorithm is a special kind of optimization technique which is highly suitable for finding the adaptive clustering for volatile networks. This Paper Aims To form a location based mobile community (LBMC) by using Ant-based clustering algorithm. Due to the mobile type community, a vanishing community problem is also stated in this paper. Instead of redo a whole algorithm again, we modify an original algorithm by applying a pheromone concept to handle a change. Our algorithm is named as ABCA & VP which stands for Ant-Based Clustering Algorithm with Vanishing problem. More than 5,000 samples from their latitude and longitude coordinates in Thailand. From an experiment, K-means clustering work well in small data size and low number of clusters. In Small size of data between 50 and 1000, our algorithm runs battery when a number of clusters reach 15 clusters. In a big data size (between 1,000 and 5,000 samples), our algorithm outperforms K-means clustering when a number of clusters reach 20 clusters.

An improved ant-based algorithm based on heaps merging and fuzzy c-means for clustering cancer gene expression data

The microarray technology enables the analysis of the gene expression data and the understanding of the important biological processes in an efficient way. We have developed an efficient clustering scheme for microarray gene expression data based on correlation-based feature selection, ant-based clustering, fuzzy c-means algorithm and a novel heaps merging heuristic. The algorithm utilizes the feature selection algorithm to overcome the high-dimensionality problem encountered in bioinformatics domain. Based on extensive empirical analysis on microarray data, clustering quality of the ant-based clustering algorithm is enhanced with the use of fuzzy c-means algorithm and heaps merging heuristic. The performance of the proposed clustering scheme is compared with k-means, PAM algorithm, CLARA, self-organizing map, hierarchical clustering, divisive analysis clustering, self-organizing tree algorithm, hybrid hierarchical clustering, consensus clustering, AntClass algorithm and fuzzy c-means clustering algorithms. The experimental results indicate that the proposed clustering scheme yields better performance in clustering cancer gene expression data.

Publishing Anonymized Set-Valued Data via Disassociation towards Analysis

Data publishing is a challenging task for privacy preservation constraints. To ensure privacy, many anonymization techniques have been proposed. They differ in terms of the mathematical properties they verify and in terms of the functional objectives expected. Disassociation is one of the techniques that aim at anonymizing of set-valued datasets (e.g., discrete locations, search and shopping items) while guaranteeing the confidentiality property known as k m -anonymity. Disassociation separates the items of an itemset in vertical chunks to create ambiguity in the original associations. In a previous work, we defined a new ant-based clustering algorithm for the disassociation technique to preserve some items associated together, called utility rules, throughout the anonymization process, for accurate analysis. In this paper, we examine the disassociated dataset in terms of knowledge extraction. To make data analysis easy on top of the anonymized dataset, we define neighbor datasets or in other terms datasets that are the result of a probabilistic re-association process. To assess the neighborhood notion set-valued datasets are formalized into trees and a tree edit distance (TED) is directly applied between these neighbors. Finally, we prove the faithfulness of the neighbors to knowledge extraction for future analysis, in the experiments.

Peintures du sommeil par des fourmis artificielles

This paper provides technical and conceptual information on a swarm art project called Sleep Paintings. The project consists of a series of colour images generated by an ant-based clustering algorithm operating with sleep data sets extracted from electroencephalogram (EEG) signals. The algorithm was originally proposed for clustering and classification and is described by a simple set of equations that model the local behavior of artificial ants (data samples) in a way that, when travelling on a heterogeneous 2-dimensional lattice of vectors, they form clusters of similar data samples, while changing the vectors of the environmental lattice. The 2-dimensional colored abstract sketches of human sleep described in the paper result from the visualization of the lattice.

Clustering with a Novel Global Harmony Search Algorithm for Image Segmentation

A state-feedback based harmony search (SFHS) algorithm to clustering data is presented to solve the problems that the traditional evolutionary-based clustering algorithms easily trap into the local optimum. Firstly, the state-feedback mechanism is introduced into HS algorithm, and the harmony memory difference metric is defined to adaptively adjust harmony memory considering rate and pitch adjusting bandwidth, which makes the convergence and efficiency of the harmony search (HS) algorithm improved obviously. Secondly, in the SFHS-based clustering algorithm, the decision variables in the harmony vector represent cluster centers, and the harmony vector represents a partition of data, the best partition of data can be obtained by updating the harmony memory. Finally, a novel validity metric is presented to determine the right number of clusters. Simulation experiments have been carried out on remote sensing images and animal images, and the relevant results are compared with the ANT-based clustering algorithm and the traditional HS-based clustering algorithms, it shows that the SFHS-based clustering algorithm has better convergence.

Bio-inspired-based approach for web services classification

Web services have become a promising technology in a variety of e-science or e-business areas. Classification of web services is one methodology that can be used in order to enhance the speed of web service discovery process. There are a variety of models and methods to deal with this issue from different aspects. Bio-inspired algorithms are becoming main approaches and solutions. This paper proposes a bio-inspired-based approach for web services classification. In our approach, web services are not classified based on services with similar features, but on services which share many interaction relations. This organisation can be used among others to guide the search algorithms compositions. Ant-based clustering algorithms are an appropriate alternative to traditional clustering algorithms. The algorithm has a number of features that make it an interesting study of cluster analysis. It has the ability of automatically discovering the number of clusters.

Fuzzy Rules for Ant Based Clustering Algorithm

This paper provides a new intelligent technique for semisupervised data clustering problem that combines the Ant System (AS) algorithm with the fuzzyc-means (FCM) clustering algorithm. Our proposed approach, called F-ASClass algorithm, is a distributed algorithm inspired by foraging behavior observed in ant colonyT. The ability of ants to find the shortest path forms the basis of our proposed approach. In the first step, several colonies of cooperating entities, called artificial ants, are used to find shortest paths in a complete graph that we called graph-data. The number of colonies used in F-ASClass is equal to the number of clusters in dataset. Hence, the partition matrix of dataset founded by artificial ants is given in the second step, to the fuzzyc-means technique in order to assign unclassified objects generated in the first step. The proposed approach is tested on artificial and real datasets, and its performance is compared with those ofK-means,K-medoid, and FCM algorithms. Experimental section shows that F-ASClass performs better according to the error rate classification, accuracy, and separation index.

Using Service Clustering and Self-Adaptive MOPSO-CD for QoS-Aware Cloud Service Selection

A promising way to effectively manage the composition of services in a heterogeneous and dynamic environment is to make workflow management able to self-adapt at runtime to react to changes in its environment by autonomously reconfiguring itself. Most of the proposed methodologies address this issue as a QoS-aware service selection problem in which a web service broker can dynamically select the “right” service that takes part in the composition, and adaptively change the bound service when the delivered QoS has changed.In this paper, we propose a self-organizing framework that uses a service clustering based discovery approach to effectively and efficiently support the selection of services in which runtime changes in the QoS of the services are taken into account. Two bio-inspired algorithms are designed to support a QoS-aware dynamic service selection mechanism. An ant-based clustering algorithm enhanced with a template mechanism that guides the artificial ants to move data items to construct and maintain a specific topology is adopted as a method for efficient service discovery. As a consequence, services can dynamically be discovered in a shorter time and with lower network traffic. To select the actual concrete services that best meet the user QoS requirements a Self-Adaptive Multi-Objective Particle Swarm Optimization Algorithm using crowding distance technique (MOPSO-CD) is executed using the topological map generated by the ant algorithm. In the end, simulation results show the effectiveness of the method proposed.

An ant-based approach to cluster peers in P2P database systems

Peer-to-Peer (P2P) systems with distinct advantages such as scalability, robustness and low cost have developed quickly in recent years. The resource search problem, that is to find out a peer having a desired resource, is a fundamental problem to be initially solved in most P2P applications. P2P systems can also be categorized under database systems. One of the most important problems of P2P database systems is the query processing algorithms. Even a rather small improvement over these algorithms can have a significant impact on the performance of a P2P database system. One of the most effective methods to reduce query processing cost is peer clustering. Hence, clustering peers with similar contents makes the queries directed to the appropriate cluster and thus to be efficiently answered. In this paper, we present a new peer clustering algorithm for P2P database systems that uses ant-based clustering algorithms. We have adjusted ant-based clustering algorithm to peer clustering problem. Performance evaluation of the algorithm shows its strength in clustering peers in P2P database systems to be of a higher quality and accuracy compared to the existing systems, to have a relatively short running time and to quickly converge to an optimum. Moreover, our algorithm achieves a high scalability to the increasing problem size.

MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier

This article introduces a hybrid approach that combines the advantages of fuzzy sets, ant-based clustering and multilayer perceptron neural networks (MLPNN) classifier, in conjunction with statistical-based feature extraction technique. An application of breast cancer MRI imaging has been chosen and hybridization system has been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: Benign or Malignant. The introduced hybrid system starts with an algorithm based on type-II fuzzy sets to enhance the contrast of the input images. This is followed by an improved version of the classical ant-based clustering algorithm, called adaptive ant-based clustering to identify target objects through an optimization methodology that maintains the optimum result during iterations. Then, more than twenty statistical-based features are extracted and normalized. Finally, a MLPNN classifier was employed to evaluate the ability of the lesion descriptors for discrimination of different regions of interest to determine whether the cancer is Benign or Malignant. To evaluate the performance of presented approach, we present tests on different breast MRI images. The experimental results obtained, show that the adaptive ant-based segmentation is superior to the classical ant-based clustering technique and the overall accuracy offered by the employed hybrid technique confirm that the effectiveness and performance of the proposed hybrid system is high.

HYBRID ANT-BASED CLUSTERING ALGORITHM WITH CLUSTER ANALYSIS TECHNIQUES

Cluster analysis is a data mining technology design ed to derive a good understanding of data to solve clustering problems by extracting useful informatio n from a large volume of mixed data elements. Recently, researchers have aimed to derive clustering algorit hms from nature’s swarm behaviors. Ant-based clustering is an approach inspired by the natural clustering a nd sorting behavior of ant colonies. In this resear ch, a hybrid ant-based clustering method is presented wit h new modifications to the original ant colony clus tering model (ACC) to enhance the operations of ants, pick ing up and dropping off data items. Ants’ decisions are supported by operating two cluster analysis methods : Agglomerative Hierarchical Clustering (AHC) and density-based clustering. The proximity function an d refinement process approaches are inspired by previous clustering methods, in addition to an adap tive threshold method. The results obtained show th at the hybrid ant-based clustering algorithm attains bette r results than the ant-based clustering Handl model ATTA-C, k-means and AHC over some real and artificial datasets and the method requires less initial information about class numbers and dataset size.

Performance Assessment for a Fleet of Machines Using a Combined Method of Ant-Based Clustering and CMAC

This paper proposes a combined method of ant-based clustering and cerebellar model articulation controller for performance assessment for a fleet of machines. A novel ant-based clustering algorithm with kernel method is used to cluster machines in a fleet. The algorithm has two features. First, a projection based on kernel principal component analysis replaces random projection to improve the efficiency. Second, the clustering is performed on the feature space after kernel mapping to improve the clustering accuracy. The algorithm can cluster machines in a self-organizing way to achieve the horizontal assessment. The vertical assessment for the single machine based on CMAC is presented. Then, how to combine the vertical and horizontal assessment results is discussed. The outlier mining method to detect abnormal machines based on the clustering results is also proposed. Cluster-based global outlying factor is suggested to measure the outlying degree of abnormal machine. Finally, the case study on axial fans shows that the combined method can give a more comprehensive assessment for fans' performance monitoring.

A novel ant-based clustering algorithm using Renyi entropy

Ant-based clustering is a type of clustering algorithm that imitates the behavior of ants. To improve the efficiency, increase the adaptability to non-Gaussian datasets and simplify the parameters of the algorithm, a novel ant-based clustering algorithm using Renyi Entropy (NAC-RE) is proposed. There are two aspects to application of Renyi entropy. Firstly, Kernel Entropy Component Analysis (KECA) is applied to modify the random projection of objects when the algorithm is run initially. This projection can create rough clusters and improve the algorithm's efficiency. Secondly, a novel ant movement model governed by Renyi entropy is proposed. The model takes each object as an ant. When the object (ant) moves to a new region, the Renyi entropy in its local neighborhood will be changed. The differential value of entropy governs whether the object should move or be moveless. The new model avoids complex parameters that have influence on the clustering results. The theoretical analysis has been conducted by kernel method to show that Renyi entropy metric is feasible and superior to distance metric. The novel algorithm was compared with other classic ones by several well-known benchmark datasets. The Friedman test with the corresponding Nemenyi test are applied to compare and conclude the algorithms’ performance The results indicate that NAC-RE can get better results for non-linearly separable datasets while its parameters are simple.

Improving constrained clustering via swarm intelligence

By simulating the clustering behavior of the real-world ant colonies, we propose in this paper a constrained ant clustering algorithm. This algorithm is embedded with the heuristic walk mechanism based on random walk to deal with the constrained clustering problems given pairwise must-link and cannot-link constraints. Experimental results show that our approach is more effective on both the synthetic datasets and the real datasets compared with the Cop-Kmeans and ant-based clustering algorithm.

A fuzzy incremental clustering approach to hybrid data discovery

We propose an incremental fuzzy clustering algorithm for hybrid data discovery. The algorithm is based on the ASM model where data items are represented by agents placed in a two dimensional grid. The agents will group themselves into clusters by making simple moves in their environment. They will try to get closer to each other if they are rather similar or to get away from each other if they are rather different. The algorithm allocates a new agent on the grid whenever a new data item arrives. At each step the new agent contacts an agent from the grid and if they are similar then they will group together in the same cluster. Whenever a new cluster is created the agents will try to merge the cluster with one of the previously created clusters. If a newly created agent does not find a similar fellow then it will start an ASM-like process in order to search for one and thus the data is clustered. Several clustering algorithms exist each with its own strengths and weaknesses. Some algorithms need an ini- tial estimation of the number of clusters (k-means, fuzzy c-means); others could often be too slow (agglomerative hi- erarchical clustering algorithms). Ant-based clustering al- gorithms often require hybridization with a classical clus- tering algorithm such as k-means. In (2) an ant-based clustering algorithm is presented. It is based on the ASM (Ants Sleeping Model) approach. In ASM, an ant has two states on a two-dimensional grid: ac- tive state and sleeping state. When the artificial ant's fitness is low, it has a higher probability to wake up and stay in active state. It will thus leave its original position to search for a more secure and comfortable position to sleep. When an ant locates a comfortable and secure position, it has a higher probability to sleep unless the surrounding environ- ment becomes less hospitable and activates it again. In (3) a Stigmergic Agent System (SAS) combining the strengths of Ant Colony Systems and Multi-Agent Systems concepts is proposed. The agents from the SAS are using both direct and indirect communication. By using direct communication the risk of getting trapped in local optima is lower. However, as showed in (16), most ant-based al- gorithms can be used only in a first phase of the clustering process because of the high number of clusters that are usu- ally produced. In a second phase a k-means-like algorithm is often used. In (16), an algorithm in which the behaviour of the arti- ficial ants is governed by fuzzy IF-THEN rules is presented. Like all ant-based clustering algorithms, no initial partition- ing of the data is needed, nor should the number of clus- ters be known in advance. The ants are capable to make their own decisions about picking up items. Hence the two phases of the classical ant-based clustering algorithm are merged into one, and k-means becomes superfluous.

An Ant Colony Clustering Algorithm Improved from ATTA

DENNEUBOURG presents the first ant-based clustering algorithm in 1991.Ant colony clustering has the characteristics of automatically clustering, high clustering accuracy, irregular cluster shapes and so on. These are important for a clustering algorithm, so ant colony clustering is arousing more and more data mining researchers’ concentration. In 2004, J. Handl together with his partners normalized the basic ant colony clustering algorithm, getting the ATTA model with superior performance. In this paper, we’ll present an algorithm improved from ATTA which we call it LCA and test LCA on UCI data sets to show its feasibility.

Performance Analysis of a Proposed Ant-Based Clustering Algorithm

In the Ant-Based Clustering Algorithm, patterns are spread throughout a grid and each ant is assigned a pattern. The ants are responsible for picking, transporting and dropping patterns on the grid. After the clustering algorithm converges, cluster recovery is done by using the positions of patterns on the grid.

A Hybrid Recommender System for Dynamic Web Users

Nowadays, providing tools that eases the interaction of users with websites is a big challenge in e-commerce. Recommender systems are useful tools which adapts the environment of websites compatible with users needs. In this paper, applying a hybrid collaboration and content based technique a model for recommendation system is proposed. Presented model works in two offline and online phases. In offline step the behavior of users’ models with a combined FCM and ant based clustering algorithm and in online step suitable recommendations extracts for presenting to active user. The model is implemented and tested as a recommender system for personalizing website of Information and Communication Technology Center” of Isfahan municipality in Iran. The results shown are promising and proved that applying more efficient clustering technique for modeling users behavior provide us with more interesting and useful patterns which consequently making the recommender system more functional and robust.