Data clustering using bacterial foraging optimization

A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets

Image segmentation is an essential phase of computer vision in which useful information is extracted from an image that can range from finding objects while moving across a room to detect abnormalities in a medical image. As image pixels are generally unlabelled, the commonly used approach for the same is clustering. This paper reviews various existing clustering based image segmentation methods. Two main clustering methods have been surveyed, namely hierarchical and partitional based clustering methods. As partitional clustering is computationally better, further study is done in the perspective of methods belonging to this class. Further, literature bifurcates the partitional based clustering methods into three categories, namely K-means based methods, histogram-based methods, and meta-heuristic based methods. The survey of various performance parameters for the quantitative evaluation of segmentation results is also included. Further, the publicly available benchmark datasets for image-segmentation are briefed.

Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm.

Adaptive clustering based on element-wised distance for distributed estimation over multi-task networks

In multitask networks, neighboring agents that belong to different clusters pursue different goals, and therefore arbitrary cooperation will lead to a degradation in estimation performance. In this paper, an adaptive clustering method is proposed for distributed estimation that enables agents to distinguish between subneighbors that belong to the same cluster and those that belong to a different cluster. This creates an appropriate degree of cooperation to improve parameter estimation accuracy, especially for the case where the prior information of a cluster is unknown. In contrast to the static and quantitative threshold that is imposed in traditional clustering methods, we devise a method for real-time clustering hypothesis detection, which is constructed through the use of a reliable adaptive clustering threshold as reference and the averaged element-wise distance between tasks as real-time clustering detection statistic. Meanwhile, we relax the clustering conditions to maintain maximum cooperation without sacrificing accuracy. Simulations are presented to compare the proposed algorithm and some traditional clustering strategies in both stationary and nonstationary environments. The effects of task difference on performance are also obtained to demonstrate the superiority of our proposed clustering strategy in terms of accuracy, robustness, and suitability.

ADOFL: Multi-Kernel-Based Adaptive Directive Operative Fractional Lion Optimisation Algorithm for Data Clustering

The progress of databases in fields such as medical, business, education, marketing, etc., is colossal because of the developments in information technology. Knowledge discovery from such concealed bulk databases is a tedious task. For this, data mining is one of the promising solutions and clustering is one of its applications. The clustering process groups the data objects related to each other in a similar cluster and diverse objects in another cluster. The literature presents many clustering algorithms for data clustering. Optimisation-based clustering algorithm is one of the recently developed algorithms for the clustering process to discover the optimal cluster based on the objective function. In our previous method, direct operative fractional lion optimisation algorithm was proposed for data clustering. In this paper, we designed a new clustering algorithm called adaptive decisive operative fractional lion (ADOFL) optimisation algorithm based on multi-kernel function. Moreover, a new fitness function called multi-kernel WL index is proposed for the selection of the best centroid point for clustering. The experimentation of the proposed ADOFL algorithm is carried out over two benchmarked datasets, Iris and Wine. The performance of the proposed ADOFL algorithm is validated over existing clustering algorithms such as particle swarm clustering (PSC) algorithm, modified PSC algorithm, lion algorithm, fractional lion algorithm, and DOFL. The result shows that the maximum clustering accuracy of 79.51 is obtained by the proposed method in data clustering.

A Modified BFGS Formula Using a Trust Region Model for Nonsmooth Convex Minimizations

This paper proposes a modified BFGS formula using a trust region model for solving nonsmooth convex minimizations by using the Moreau-Yosida regularization (smoothing) approach and a new secant equation with a BFGS update formula. Our algorithm uses the function value information and gradient value information to compute the Hessian. The Hessian matrix is updated by the BFGS formula rather than using second-order information of the function, thus decreasing the workload and time involved in the computation. Under suitable conditions, the algorithm converges globally to an optimal solution. Numerical results show that this algorithm can successfully solve nonsmooth unconstrained convex problems.

A novel artificial bee colony based clustering algorithm for categorical data

Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee colony approach. In our approach, we first introduce a one-step k-modes procedure, and then integrate this procedure with the artificial bee colony approach to deal with categorical data. In the search process performed by scout bees, we adopt the multi-source search inspired by the idea of batch processing to accelerate the convergence of ABC-K-Modes. The performance of ABC-K-Modes is evaluated by a series of experiments in comparison with that of the other popular algorithms for categorical data.

Recent Advances in Support Vector Clustering: Theory and Applications

As an important boundary-based clustering algorithm, support vector clustering (SVC) can benefit many real applications owing to its capability of handling arbitrary cluster shapes, especially those directly or indirectly related to pattern exploration and description. As the application deepens, the importance of performance (i.e. criterions of accuracy and efficiency) of SVC increases. To identify gaps in the current methods and propose novel research directions for SVC, we present a survey of the literature in this area. Our approach is to classify the most recent advances into either theory or application. For theoretical contributions, advances related to parameter selection and optimization, dual-problem solutions, and cluster labeling are introduced. We also simultaneously summarize the advantages and drawbacks of each study. With respect to applications, we clearly describe eight groups of schemes based on SVC, either as individual or hybrid methods. Finally, we identify the gaps in SVC research and suggest several future research issues and trends.

A Clustering Approach for the l-Diversity Model in Privacy Preserving Data Mining Using Fractional Calculus-Bacterial Foraging Optimization Algorithm

In privacy preserving data mining, the l-diversity and k-anonymity models are the most widely used for preserving the sensitive private information of an individual. Out of these two, l-diversity model gives better privacy and lesser information loss as compared to the k-anonymity model. In addition, we observe that numerous clustering algorithms have been proposed in data mining, namely, k-means, PSO, ACO, and BFO. Amongst them, the BFO algorithm is more stable and faster as compared to all others except k-means. However, BFO algorithm suffers from poor convergence behavior as compared to other optimization algorithms. We also observed that the current literature lacks any approaches that apply BFO with l-diversity model to realize privacy preservation in data mining. Motivated by this observation, we propose here an approach that uses fractional calculus (FC) in the chemotaxis step of the BFO algorithm. The FC is used to boost the computational performance of the algorithm. We also evaluate our proposed FC-BFO and BFO algorithms empirically, focusing on information loss and execution time as vital metrics. The experimental evaluation shows that our proposed FC-BFO algorithm derives an optimal cluster as compared to the original BFO algorithm and existing clustering algorithms.