clustering of data is a fundamental data analysis step that has been widely studied across in data mining. Adaptive resonance theory network (ART) is an important algorithm in clustering. ART is also very popular in t...
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ISBN:
(纸本)3540343857
clustering of data is a fundamental data analysis step that has been widely studied across in data mining. Adaptive resonance theory network (ART) is an important algorithm in clustering. ART is also very popular in the unsupervised neural network. Type I adaptive resonance theory network (ART1) deals with the binary numerical data, whereas type II adaptive resonance theory network (ART2) deals with the general numerical data. Several information systems collect the mixing type attitudes, which included numeric attributes and categorical attributes. However, ART1 and ART2 do not deal with mixed data. If the categorical data attributes are transferred to the binary data format, the binary data do not reflect the similar degree. It influences the clustering quality. Therefore, this paper proposes a modified adaptive resonance theory network (M-ART) and the conceptual hierarchy tree to solve similar degrees of mixed data. This paper utilizes artificial simulation materials and collects a piece of actual data about the family income to do experiments. The results show that the M-ART algorithm can process the mixed data and has a great effect on clustering.
Automatic Finger classification is an important part of Fingerprint Automatic Identification System (FAIS). Its function is to provide a search system for large size database. Accurate classification can reduce search...
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ISBN:
(纸本)1424403316
Automatic Finger classification is an important part of Fingerprint Automatic Identification System (FAIS). Its function is to provide a search system for large size database. Accurate classification can reduce searching time and expediate matching speed. Support Vector Machine (SVM) is a new learning technique based on Statistical Learning Theory (SLT). SVM was originally developed for two-class classification. It was extended to solve multi-class classification problem. A hierarchical SVM with clustering algorithm based on stepwise decomposition was established to intellectively classify 5 classes of fingerprints. The design principle was proposed and the classification algorithm was implemented. SVM not only has more solid theoretical foundation, it also has greater generalization ability as our experiment demonstrates. The experimental results show that: SVM is effective and surpasses other classical classification techniques.
The paper discusses a clustering-based intrusion detection algorithm. The basic idea of the algorithm is the data that has same characters congregate each other by the process of volatile scale till almost overlap cen...
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The paper discusses a clustering-based intrusion detection algorithm. The basic idea of the algorithm is the data that has same characters congregate each other by the process of volatile scale till almost overlap center of a certain cluster. The benefit of the algorithm is that it needn't train data and name parameter artificially. Using the data sets of KDD99, the result of the experiment shows that this approach can detect known and unknown intrusions efficiently and correctly in the real network connections.
Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communi...
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Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.
In data mining, clustering is used to discover groups and identify interesting distribution in the underlying data. We propose a new clustering algorithm called CMR that is more robust to outliers, and identifies clus...
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ISBN:
(纸本)9812565329
In data mining, clustering is used to discover groups and identify interesting distribution in the underlying data. We propose a new clustering algorithm called CMR that is more robust to outliers, and identifies clusters having non-spherical shapes and wide variances in size. CMR achieves those by representing each cluster by multiple representatives. Having more than one representative per cluster allows CMR to adjust well to the geometry of non-spherical shapes. CMR use atomic-clustering algorithm to get atomic clusters on which the hierarchical merging algorithm is performed. CMR is a polynomial-time clustering algorithm, and so it facilitates the clustering of a very large data set.
Organization is an effective solution mode in multi-agent system. Cooperation in an agent organization is defined. Based on simple definitions, mathematical formulation of agent organizational cooperation network is *...
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Organization is an effective solution mode in multi-agent system. Cooperation in an agent organization is defined. Based on simple definitions, mathematical formulation of agent organizational cooperation network is *** clustering algorithm is applied to design optimal cooperation network. The process of designing cooperation network among agents is analyzed and compared with a case in different conditions.
clustering methods have been often used to find biologically relevant groups of genes or conditions based on their expression levels. Since many functionally related genes tend to be co-expressed, by identifying group...
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clustering methods have been often used to find biologically relevant groups of genes or conditions based on their expression levels. Since many functionally related genes tend to be co-expressed, by identifying groups of genes with similar expression profiles, the functionalities of unknown genes can be inferred from those of known genes in the same group. In this paper we address a novel clustering approach, called seed-based clustering, where seed genes are first systematically chosen by computational analysis of their expression profiles, and then the clusters are generated by using the seed genes as initial values for k-means clustering. The seed-based clustering method has strong mathematical foundations and requires only a few matrix computations for seed extraction. As a result, it provides stability of clustering results by eliminating randomness in the selection of initial values for cluster generation. Our empirical results reported here indicate that the entire, clustering process can be systematically pursued using seedbased clustering, and that its performance is favorable compared to current approaches.
Conventional clustering algorithms are designed for a single independent dataset, i.e. Fuzzy C-Means (FCM) clustering algorithm. In the real world, a dataset is independent of other datasets but sometimes can be coope...
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Conventional clustering algorithms are designed for a single independent dataset, i.e. Fuzzy C-Means (FCM) clustering algorithm. In the real world, a dataset is independent of other datasets but sometimes can be cooperative with others by exchanging information, such as the relationship between subsidiary companies. We should therefore consider the influence from other relative collaborative datasets while performing clustering learning under such collaborative circumstances. In this paper, three different collaborative models are discussed and new correct methods are proposed to quantitatively measure such collaboration between datasets, i.e. information gain. The corresponding collaborative clustering algorithms are presented accordingly and the theoretical analysis shows that the new cooperative clustering algorithms can finally converge to a local minimum. Experimental results demonstrate that the clustering structures obtained by new cooperative algorithms are different from those of conventional algorithms for the consideration of collaboration and the performances of these collaborative clustering algorithms can be much better than those conventional "single" clustering algorithms under the cooperating circumstances.
Sensor networks require scalable solutions to tackle huge number of sensor nodes. The scalable solutions have to be energy efficient and robust in order that reliability and network lifetime is increased. Hierarchical...
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ISBN:
(纸本)078039206X
Sensor networks require scalable solutions to tackle huge number of sensor nodes. The scalable solutions have to be energy efficient and robust in order that reliability and network lifetime is increased. Hierarchical (cluster based) models are known to be promising solutions to the problem of scalability. In this paper we propose a distributed clustering strategy which restricts the number of nodes in each cluster, S and also limits the number of next hop neighbours of a node in a cluster, D (admissible degree). The clustering algorithm is simulated and compared with two other clustering algorithms, HEED and LEACH. Simulation results depict that the energy drain in our algorithm is much lesser than the other two algorithms, thereby providing a longer lifetime v.,hen compared to the other algorithms. The proposed algorithm provides for almost 5 times increase in the lifetime of the network, when compared to the other two algorithms.
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