Document clustering is the process of organizing a particular electronic corpus of documents into subgroups of similar text features. Previously, a number of statistical algorithms had been applied to perform clustering to the data including the text documents. There are recent endeavors to enhance the performance of the clustering with the optimization based algorithms such as the evolutionary algorithms. Thus, document clustering with evolutionary algorithms became an emerging topic that gained more attention in the recent years. This paper presents an up-to-date review fully devoted to evolutionary algorithms designed for document clustering. Its firstly provides comprehensive inspection to the document clustering model revealing its various components and related concepts. Then it shows and analyzes the principle research work in this topic. Finally, it brings together and classifies various objective functions from the collection of research papers. The paper ends up by addressing some important issues and challenges that can be subject of future work.
The objective function (or fitness function) is the measure that evaluates the optimality of the generated evolutionary algorithm solutions in the search space. In clustering domain, the fitness function refers to the adequacy of the partitioning. Accordingly, it needs to be formulated carefully, taken into consideration that the clustering is an unsupervised process.
Different objective functions generate different solutions even form the same evolutionary algorithm. Presuming also that the fitness could either be a minimization or a maximization function. Moreover, the algorithm could be formulated with one or with multi objective functions. To sum up, "choosing optimizati...
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This paper has presented a multiple ontology query processing method and analyzed case studies on domain-specific ontology based query expansion. Use of ontologies for information retrieval, in particular their use in the area of query expansion is presented. Concept-based query expansion retaining original keywords yields more desirable and useful results. Compound words add complexity to the query expansion, however further research experiments are desirable to study the effects of using ontology for query expansion. Finally further research is outlined for the exploit of ontology based information retrieval in Cloud.
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...o cut. The brief idea is clustering is done around half data through Hierarchical clustering and succeed by K-means for the remaining. In order to create super-rules, Hierarchical is terminated when it generates the largest number of clusters.
Chapter-5 describes my whole work i.e. generation of testcases using genetic algorithm. Process of the generation of test cases is given. How the factors described can help in finding fitness function. Operators used by genetic algorithms are described.
Clustering algorithms can be categorized based on their cluster model. The most appropriate clustering algorithm for a particular problem often needs to be chosen experimentally. It should be designed for one kind of models has no chance on a data set that contains a radically different kind of models. For example, k-means cannot find non-convex clusters. Difference between classification and clustering are two common data mining techniques for finding hidden patterns in data. While the classification and clustering is often me...
I have always been fascinated by Biology and Computer Science which propelled me to take up my undergraduate studies in the field of Bioinformatics. As a part of my undergraduate curriculum, I have been exposed to a variety of subjects such as “Introduction to Algorithms”, “System Biology”, “PERL for Bioinformatics”, “Python”, “Structure and Molecular Modeling” and “Genomics and Proteomics” which had invoked my interest in areas such as docking algorithms, protein structure prediction, practical aspects of setting and running simulation, gene expression prediction through computational analysis. These fields have both a strong computational flavour as well as the potential for research which is what attracts me towards them.
A major concern in optimization is distinguishing involving global and local optima. Each other factors living being equal, one would ever more want a globally optimal solution to the optimization problem. It might not be achievable to get a global solution with one should be satisfied with obtaining a limited solution.
Genetic algorithms are a randomized search method based on the biological model of evolution through mating and mutation. In the classic genetic algorithm, problem solutions are encoded into bit strings which are tested for fitness, then the best bit strings are combined to form new solutions using methods which mimic the Darwinian process of "survival of the fittest" and the exchange of DNA which occurs during mating in biological systems. The programming of genetic algorithms involves little more than bit manipulation and scoring the quality of solutions. Genetic algorithms have been applied to problems as diverse as graph partitioning and the automatic creation of programs to match mathematical functions.
Optimization, in simple terms, means minimize the cost incurred and maximize the profit such as resource utilization. EAs are population based metaheuristic (means optimize problem by iteratively trying to improve the solution with regards to the given measure of quality) optimization algorithms that often perform well on approximating solutions to all types of problem because they do not make any assumptions about the underlying evaluation of the fitness function. There are many EAs available viz. Genetic Algorithm (GA) [1] , Artificial Immune Algorithm (AIA) [2], Ant Colony Optimization (ACO) [3], Particle Swarm Optimization (PSO) [4], Differential Evolution (DE) [5, 6], Harmony Search (HS) [7], Bacteria Foraging Optimization (BFO) [8], Shuffled Frog Leaping (SFL) [9], Artificial Bee Colony (ABC) [10, 11], Biogeography-Based Optimization (BBO) [12], Gravitational Search Algorithm (GSA) [13], Grenade Explosion Method (GEM) [14] etc. To use any EA, a model of decision problem need to be built that specifies: 1) The decisions to be made, called decision variables, 2) The measure to be optimized, called the objective, and 3) Any logical restrictions on potential solutions, called constraints. These 3 parameters are necessary while building any optimization model. The solver will find values for the decision variables that satisfy the constraints while optimizing (maximizing or minimizing) the objective. But the problem with all the above EAs is that, to get optimal solution, besides the necessary parameters (explained above), many algorithms-specific parameters need to be handled appropriately. For example, in case of GA, adjustment of the algorithm-specific parameters such as crossover rate (or probability, PC), mu...
...ion is that clustering is an “unsupervised” activity while classification is a supervised one. In clustering, there is no one who assigns documents to classes but it is only the distribution and makeup of the data that will determine cluster membership (Manning et al., 2008).
The concept cluster analysis includes a number algorithms and methods for grouping objects of similar kind into respective categories. From a general question it could face many areas of inquiry and how to organize observed data into meaningful structures. (TIBCO, 2018) . In other words, cluster analysis is an organizational method, which helps to perform and sort a pattern in between the data. It also allows you to find the connection between two objects distinct from each other or their same characteristics.
When an algorithm is applied to solve a classification problem with a different set of parameters, the classification accuracy also differs abruptly in each case . The challenge in machine learning to find the most suitable parameter values of the algorithms that solves an engineering problem to the best possible way in terms of performance metrics. Therefore, one has to fine tune the algorithm parameters that best suits the problem. There are several optimization techniques like genetic algorithm, particle swarm optimization , Tabu search methods etc. The focus of the study is to calibrate the algorithm parameters using design of experiment
Optimization techniques are reflected as one of the finest techniques for finding optimal design using machines. Multi-objective optimization “The main focus of this work” deals with finding solutions for problems having more than one objectives. And obviously there is more than one solution for
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However, the optimization carried by GA technology requires a better setting of its own parameters, such as number of generations, population size, etc. Otherwise, there may be a risk of an insufficient sweeping of the search space system. In addition, it is suggested the use of conventional projects to research the space around the conditions found by the GA, in order to obtain models and/or perform a fine-tuning of the optimal parameters.