A Generalized Enhanced Quantum Fuzzy Approach for Efficient Data Clustering

© 2013 IEEE. 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

Nature-inspired optimization algorithms for text document clustering—a comprehensive analysis

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Text clustering is one of the efficient unsupervised learning techniques used to partition a huge number of text documents into a subset of clusters. In which, each cluster contains similar documents and the clusters contain dissimilar text documents. Nature-inspired optimization algorithms have been successfully used to solve various optimization problems, including text document clustering problems. In this paper, a comprehensive review is presented to show the most related nature-inspired algorithms that have been used in solving the text clustering problem. Moreover, comprehensive experiments are conducted and analyzed to show the performance of the common well-know nature-inspired optimization algorithms in solving the text document clustering problems including Harmony Search (HS) Algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Ant Colony Optimization (ACO), Krill Herd Algorithm (KHA), Cuckoo Search (CS) Algorithm, Gray Wolf Optimizer (GWO), and Bat-inspired Algorithm (BA). Seven text benchmark datasets are used to validate the performance of the tested algorithms. The results showed that the performance of the well-known nurture-inspired optimization algorithms almost the same with slight differences. For improvement purposes, new modified versions of the tested algorithms can be proposed and tested to tackle the text clustering problems

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

A Hybrid Chimp Optimization Algorithm and Generalized Normal Distribution Algorithm with Opposition-Based Learning Strategy for Solving Data Clustering Problems

This paper is concerned with data clustering to separate clusters based on the connectivity principle for categorizing similar and dissimilar data into different groups. Although classical clustering algorithms such as K-means are efficient techniques, they often trap in local optima and have a slow convergence rate in solving high-dimensional problems. To address these issues, many successful meta-heuristic optimization algorithms and intelligence-based methods have been introduced to attain the optimal solution in a reasonable time. They are designed to escape from a local optimum problem by allowing flexible movements or random behaviors. In this study, we attempt to conceptualize a powerful approach using the three main components: Chimp Optimization Algorithm (ChOA), Generalized Normal Distribution Algorithm (GNDA), and Opposition-Based Learning (OBL) method. Firstly, two versions of ChOA with two different independent groups' strategies and seven chaotic maps, entitled ChOA(I) and ChOA(II), are presented to achieve the best possible result for data clustering purposes. Secondly, a novel combination of ChOA and GNDA algorithms with the OBL strategy is devised to solve the major shortcomings of the original algorithms. Lastly, the proposed ChOAGNDA method is a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can be used to tackle large and complex real-world optimization problems, particularly data clustering applications. The results are evaluated against seven popular meta-heuristic optimization algorithms and eight recent state-of-the-art clustering techniques. Experimental results illustrate that the proposed work significantly outperforms other existing methods in terms of the achievement in minimizing the Sum of Intra-Cluster Distances (SICD), obtaining the lowest Error Rate (ER), accelerating the convergence speed, and finding the optimal cluster centers.Comment: 48 pages, 14 Tables, 12 Figure

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Evolutionary dataset optimisation: learning algorithm quality through evolution

In this paper we propose a new method for learning how algorithms perform. Classically, algorithms are compared on a finite number of existing (or newly simulated) benchmark data sets based on some fixed metrics. The algorithm(s) with the smallest value of this metric are chosen to be the `best performing'. We offer a new approach to flip this paradigm. We instead aim to gain a richer picture of the performance of an algorithm by generating artificial data through genetic evolution, the purpose of which is to create populations of datasets for which a particular algorithm performs well. These data sets can be studied to learn as to what attributes lead to a particular progress of a given algorithm. Following a detailed description of the algorithm as well as a brief description of an open source implementation, a number of numeric experiments are presented to show the performance of the method which we call Evolutionary Dataset Optimisation

A Review of Research Methodologies Employed in Serendipity Studies in the Context of Information Research

Background: The concept of serendipity has become increasingly interesting for those undertaking serendipity research in recent years. However, serendipitous encounters are subjective and rare in a real-world context, making this an extremely challenging subject to study. Methods: Various methods have been proposed to enable researchers to understand and measure serendipity, but there is no broad consensus on which methods to use in different experimental settings. A comprehensive literature review was first conducted, which summarizes the research methods being employed to study serendipity. It was followed by a series of interviews with experts that specified the relative strengths and weaknesses of each method identified in the literature review, in addition to the challenges usually confronted in serendipity research. Results: The findings suggest using mixed research methods to produce a more complete picture of serendipity and contribute to the verification of any research findings. Several challenges and implications relating to empirical studies in the investigation of serendipity have been derived from this study. Conclusions: This paper investigated research methods employed to study serendipity by synthesizing finding from a literature review and the interviews with experts. It provides a methodological contribution to serendipity studies by systematically summarizing the methods employed in the studies of serendipity and identifying the strengths and weakness of each method. It also suggests the novel approach of using mixed research methods to study serendipity. This study has potential limitations related to a small number of experts involved in the expert interview. However, it should be noted that the nature of the topic is a relatively focused area, and it was observed after interviewing the experts that new data seems to not contribute to the findings owing to its repetition of comment