The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
With the wide application of GIS to all kinds of fields, and developing of the technique of data mining and spatial data collection, the technique of data mining in spatial database-spatial data mining is coming out. In order to satisfy the people's demand for the interesting and potentially useful knowledge from the spatial database, this thesis used a wide using spatial clustering algorithm: k-means...
Extracting association rules from data with both discrete and continuous attributes is an important problem in KDD. A new model of immune genetic algorithm is formulated for solving this problem. This algorithm uses three-segment chromosomes, integrating the discretization, attributes reduction and mining association rules. And immune mechanism is introduced into genetic algorithm to avoid premature...
In the Network Intrusion Detection, the large number of features increases the time and space cost, besides the irrelative redundant characteristics make the detection accuracy dropped. In order to improve detection accuracy and efficiency, a new Feature Selection method based on Rough Sets and improved Genetic Algorithms is proposed for Network Intrusion Detection. Firstly, the features are filtered...
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...
In data mining, the classification algorithms usually pursue more highly accuracy. It is based on the assumption that all misclassifications have the same cost. Obvious, the assumption is not suitable. By improving the encode/decode methods and taking different misclassification cost into account, this paper concerns a new cost-sensitive algorithm called CS-GE based on Gene Expression. The experimental...
For classification problems in data mining based on the thought of combination classification method, this paper proposes a combination classification method of multiple decision trees, which was based on genetic algorithm. In the proposed combination classification method, multiple decision trees that adopt the method of probability measurement level output are parallel combined. Then genetic algorithm...
That traditional K-mean algorithm is a widely used clustering algorithm, with a wide application. In light of the disadvantage of K-mean algorithm, improvement is made to the traditional K-mean algorithm, a k value learning algorithm is proposed. Using genetic algorithm to optimize the K value, and improve clustering performance.
Because sugarcane average unit yield was affected by multiple factors in its growth and its inherent law was lack of external correlation data mining, the precise of the prediction method was low. Recently, the adaptive of modern intelligent genetic neural network algorithm for multi-factor effect has been strong, and the prediction accuracy has been high, but with which in sugarcane average unit...
Clustering is an important research topic in data mining that appears in a wide range of unsupervised classification applications. Partitional clustering algorithms such as the k-means algorithm are the most popular for clustering large datasets. The major problem with the k-means algorithm is that it is sensitive to the selection of the initial partitions and it may converge to local optima. In this...
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...
A non-dominated multiobjective evolutionary algorithm for extracting fuzzy rules in subgroup discovery (NMEEF-SD) is described and analyzed in this paper. This algorithm, which is based on the hybridization between fuzzy logic and genetic algorithms, deals with subgroup-discovery problems in order to extract novel and interpretable fuzzy rules of interest, and the evolutionary fuzzy system NMEEF-SD...
Efficient application assignment algorithm is important for high performance and low power consumption in NoC architecture. In this paper, we apply novel algorithm based on GA (genetic algorithm) and maximal free matrix constraint, which aim at using confliction avoidance and minimization between router communications in order to provide less network contentions during several running applications...
A general new methodology using evolutionary algorithm viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi Objective Particle Swarm Optimization (MOPSO) for obtaining optimal tolerance allocation and alternative process selection for mechanical assembly is presented. The problem has a multi-criterion character in which 3 objective functions, 6 constraints and 11 variables are...
Optimization problems are ubiquitous and consequential. In fact every sphere of human activity that can be quantified can be formulated as an optimization problem. The focus of this work is on Global Optimization which is not only desirable but also necessary in many cases. In the past few decades several Global optimization algorithms have been suggested in literature out of which stochastic, population...
In this paper, we concentrate on efficiency of path finding problem with obstacles. The implemented algorithms for solving such a problem are based on ant colony optimization, genetic algorithm idea, simulated annealing and simple algorithms. The simple algorithms like random search and Nai??ve algorithm are considered either. As the investigation tool we have designed and implemented computer experimentation...
Crossover operation is the main means of Genetic Algorithms, for the lack of crossover operation, from three aspects of crossover operation, systemically proposed one kind of improved Crossover operation of Genetic Algorithms, namely used a kind of new consistent Crossover Operator and determined which two individuals to be paired for crossover based on relevance index, which can enhance the algorithm's...
As belt transmission can offer a maximum of versatility as power transmission elements and allow the designer considerable flexibility in selecting a location of driver and driven machinery, and can operate smoothly and silently, therefore it is very necessary to use advanced methods to design the belt transmission. Considering the random character of the design parameters and load-bearing capacity,...
The particle swarm optimization (PSO) algorithm is vulnerable to reach local optimal value. So, this paper presents an adaptive hybrid particles swarm optimization. During the solving process, both crossover operator in genetic algorithm and hyper-mutation are introduced. Referring to the selection mechanism of immune algorithm based on information entropy, the adaptive selections mechanism is proposed...
In this paper an improved genetic algorithm is proposed to solve optimal problems applying fixed-point algorithms of continuous self-mapping in Euclidean space. The algorithm operates on an J1 subdivision of searching space and generates the integer labels at the vertices, and then crossover operators and mutation operators relying on the integer labels are designed. In this case, whether every individual...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.