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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1. What is the name of the document? Ida Tarbell Criticizes Standard Oil (1904) 2. What type of document is it? (newspaper, map, image, report, Congressional record, etc.)
Kay Arthur teaches how to recognize key words and phrases by creating lists, summarizing chapt...
−→ C = 2 −→ r 2 (14) where components of −→ a are linearly decreased from 2 to 0 over the course of iterations and r 1 , r2 are random vectors in [0, 1]. The hunt is usually guided by the alpha. The beta and delta might also participate in hunting occasionally. In order to mathematically simulate the hunting behavior of grey wolves, the alpha (best candidate solution) beta, and delta are assumed to have better knowledge about the potential location of prey. The first three best solutions obtained so far and oblige the other search agents (including the omegas) to update their positions according to the position of the best search agents.
The input of algorithm is Data points with n features and the number of clusters given by K. Initially K centroids are assigned randomly. The points in the dataset are assigned to a cluster based on Euclidean distance.
...means and become familiar with K-means clustering and its usage. Then, we finish this part by different method of clustering. The K-nearest- neighbors is also discussed in this chapter. The KNN is simple for implication, programming, and one of the oldest techniques of data clustering as well. There are many applications existing for KNN and it is still growing. The PCA also discussed in this chapter as a method for dimension reduction, and then discrete wavelet transform is discussed. For the next chapter the combination of PCA and DWT, which can be useful in de-noising, come about. In this study, we have examined the neural network structure and modeling that is most of usage these days. The backpropagation is one of the common methods of training neural networks and for the last model, we discussed autoregressive model and the strategies to choose a model order.
Darwin has two theories on the key principles of theory of evolution. One is the natural selection, a species that attains characteristics that are adapted to their environments (Darwin, Charles). The other one is survival of the fittest, which is when an individual best adapts to their environment survive to reproduce, and their genes are passed to later generat...
Information Retrieval (IR) is to represent, retrieve from storage and organise the information. The information should be easily access. User will be more interested with easy access information. Information retrieval process is the skills of searching for documents, for information within documents and for metadata about documents, as well as that of searching relational databases and the World Wide Web. According to (Shing Ping Tucker, 2008), E-commerce is rapidly a growing segment in the internet.
In today’s fast paced technology, search engines have become vastly popular use for people’s daily routines. A search engine is an information retrieval system that allows someone to search the...
Many times it happens that one gets stubborn and decides that it must always appear first for a certain keywords. This attitude can lead you to spend hundreds of euros since the end keywords with higher bids, are precisely constantly increase the price of the bids. It's like the snake that bites its tail.
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.
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...
... applied on different Domain data sets and sub level data sets. The data sets are applied on Maximum entropy, Support Vector Machine Method, Multinomial naïve bayes algorithms, I got 60-70% of accuracy. The above is also applied for the Unigrams of Maximum entropy, Support Vector Machine Method, Multinomial naïve bayes algorithms achieved an accuracy of 65-75%. Applied the same data on proposed lexicon Based Semantic Orientation Analysis Algorithm, we received better accuracy of 85%. In subjective Feature Relation Networks Chi-square model using n-grams, POS tagging by applying linguistic rules performed with highest accuracy of 80% to 93% significantly better than traditional naïve bayes with unigram model. The after applying proposed model on different sets the results are validated with test data and proved our methods are more accurate than the other methods.
Jurafsky, D. & Martin, J. H. (2009), Speech and Language Processing: International Version: an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 2nd ed, Pearson Education Inc, Upper Saddle River, New Jersey.
The convergence of the GA to a suitable solution depends on its basic parameter like reproduction, crossover, mutation, selection and population; which to find a relationship among them to maintain search robust...
Natural selection is based on the concept “survival of the fittest” where the most favourable individual best suited in the environment survive and pass on their genes for the next generation. Those individual who are less suited to the environment will die.