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There are some atypical texts hidden in the Telecom's customer complaint text. These atypical complaints can be divided into several classes. Atypical items have high confidence with intergroup neighbor, but low support in full complaint set. After filtering out the high-frequency items, we can use k-means method to clustering the complaint texts. However, the clustering result is affected by the...
Automatic Manifold identification is currently a challenging problem in Machine Learning. This process consists on separating a dataset blindly, according to the form defined by the data instances in the space. Data are discriminated in groups defined by their form. These approaches are usually focused on continuity-based methods where the manifold follows a continuity criterion. Currently, clustering...
In our previous study, a grouping-geneticalgorithm- based (GGA-based) attribute clustering process has been proposed for grouping features. In this paper, we further improve its performance and propose a center-based GGA for attribute clustering (CGGA). A new encoding scheme with corresponding crossover and mutation operators are designed, and an improved fitness function is proposed to achieve better...
In the past, the concept of performing the task of feature selection by attribute clustering was proposed. Hong et al. thus proposed several genetic algorithms for finding appropriate attribute clusters. In this paper, we attempt to improve the performance of the GA-based attribute-clustering process based on the grouping genetic algorithm (GGA). In our approach, the general GGA representation and...
Genetic Algorithm and Association Rules both are commonly used methods in data mining. In this paper, a brief overview of Genetic Algorithm and Association Rules has been given, and this paper has presented an improved extract method of association rules of genetic algorithm based on their respective advantages and disadvantages. It also did some research on designing encoding methods, structuring...
To mine popular accessed Web pages items and find out their association rule from the Web server Log database for junior users providing recommendation service. A novel GEP-based algorithm for mining multiple-layers association rules was presented. Firstly, takes generalizing technology as a way to value fitness function in GEP (Gene Expression Programming). Then, relying on the significant self-search...
In interactive genetic algorithms, user's fatigue is the core problem. Aiming at this problem, implicit knowledge which presents the user's variational preference is extracted to direct the evolution, at the same time, the speed of convergence is improved. Using frequent pattern algorithms to mine the implicit knowledge, frequent patterns toting the knowledge are extracted for every certain generations...
This paper presents the G3PARM algorithm for mining representative association rules. G3PARM is an evolutionary algorithm that uses G3P (Grammar Guided Genetic Programming) and an auxiliary population made up of its best individuals who will then act as parents for the next generation. Due to the nature of G3P, the G3PARM algorithm allows us to obtain valid individuals by defining them through a context-free...
Association rules are import basis of describing Web users' behavior characteristic. Traditional algorithms of Web association rules mining, based on statistics, usually pays attention to the analysis on existing data,they can't offer effective predictive means and optimizing measure and can not find out the latent and possible rules. This paper presents a kind of system of the Web association rules...
The commercial banks need identify exceptional client in their large number of customers to prevent abnormal customer's risk. In this paper, four types of abnormal data detection method is introduced, present a new method- the k-medoids clustering algorithm combining genetic algorithm to detect the outlier. Finally, apply the algorithm to analysis credit data sets, detect outlier and identify abnormal...
Text Categorization is an important research branch in the data mining domain. In this paper, an improved Naive Bayesian Classifier which is based on the Genetic Algorithms is proposed. It can make an effective Naive Bayesian classifier with excellent attributes Set in the field of text categorization. The experiments show that this method has a good classification performance.
The angle of break is a key factor that determines the mining damage extent of the surface in a mine, and it is also used to depict the characteristics of the mining subsidence basin. The geological and mining factors that influence the angle of break are fully analyzed. Based on the practical observational data from the ground movement monitoring stations of many mines in China, a neural network...
This paper proposes an effective clustering algorithm for databases, which are benchmark data sets of data mining applications. We present a Genetic Clustering Algorithm (GCA) that finds a globally optimal partition of a given data sets into a specified number of clusters. The algorithm is distance-based and creates centroids. To evaluate the proposed algorithm, we use some artificial data sets and...
Association rule mining based on support and confidence generates a large number of rules. However, post analysis is required to obtain interesting rules as many of the generated rules are useless. We pose mining association rules as multi-objective optimization problem where objective functions are rule interestingness measures and use NSGA-II, a well known multi-objective evolutionary algorithm...
Differential evolution (DE) algorithm is a heuristic approach that gains more interest in today's research. It finds the true global minimum regardless of the initial parameter values, fast convergence, and using few control parameters. DE algorithm is a population based algorithm like genetic algorithm using similar operators; crossover, mutation and selection. This paper addresses the restrictive...
In this paper, we modify genetic algorithm (GA) with new strategies of population partitioning and space reduction for high dimensional problems. The proposed method is called GA with matrix-coding partitioning (GAMCP). In the GAMCP method, a population of chromosomes is coded in a one big matrix. This matrix is partitioned into several sub-matrices every generation, and GAMCP applies the genetic...
Now, autonomous tasks planning and allocating (TPA) in Multi Agent System (MAS) has been one key and fundamental problem to promote the intelligent level of such system. Autonomous TPA means that, all tasks should be (re)planned and (re)allocated automatically according to the synthesis constraints and the dynamic environment aspects, such as the changing mission, status of each member, and topology,...
This paper presents a novel discrete population based stochastic optimization algorithm inspired from weed colonization. Its performance in a discrete benchmark, time-cost trade-off (TCT) problem, is evaluated and compared with five other evolutionary algorithms. Also we use our proposed discrete invasive weed optimization (DIWO) algorithm for cooperative multiple task assignment of unmanned aerial...
After analysis the characteristics of AlS-based intrusion detection system, a new AlS-based intrusion detection model based improved genetic algorithm is established. By utilizing prominent characteristics of genetic algorithm, such as automatic optimizing, global researching, and adaptability, the new model uses genetic operator to improve the candidate detectors generating algorithm and reduce detectors...
Since its foundation in 1994, the grouping genetic algorithm (GGA) is the only evolutionary algorithm heavily modified to suit the structure of grouping problems. In this paper we design the grouping version of evolution strategies (ES). It is well-known that ES maintains a Gaussian mutation, recombination and a selection operator for optimizing non-linear continuous functions. Therefore, the development...
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