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First, we classify the objects in continuous domain decision table according to fuzzy clustering; then, combining rough set theory with fuzzy set theory, an attribute reduct algorithm of decision table with continuous attributes is put forward; at last, a rule extraction algorithm is proposed and also the validity of this algorithm is accounted for through an example.
Aiming at the knowledge mining from fuzzy and uncertain information, the definition mode and properties of the fuzzy formal context are discussed in the paper. The method of constructing the fuzzy concept lattice of the fuzzy formal context is proposed, in that the definition of fuzzy product concept is the core: the intents of two concepts are combined to form the intent of the product concept; using...
To improve the intelligibility and efficiency of knowledge expression for the land evaluation, a land evaluation method combining simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. To reduce the complexity of the land evaluation models and improve the efficiency and intelligibility of fuzzy classification association rules further, an algorithm to eliminate...
A new clustering classification approach based on fuzzy closeness relationship (FCR) is studied in this paper. As we know, fuzzy clustering classification is one of important and valid methods to knowledge discovery. One of problems in fuzzy clustering classification is to determine a certain fuzzy sample classification in given limited sample space. Another is its validity, that is to say, if the...
Aiming to these presented frequent neighboring class set mining algorithms have some repeated computing and redundancy candidate frequent neighboring class set when these algorithms extract frequent neighboring class set, this paper proposes an algorithm of mining frequent neighboring class set based on increasing sequence, which is suitable for mining frequent neighboring class set of objects in...
The purpose of this paper is to establish sample classification algorithms in consistent and inconsistent decision tables. First, according to the definition of inclusion degree and the idea of positive region in rough set, we give the definition of set value vector inclusion degree; then, according to the maximum inclusion degree principle, the sample classification algorithms are put forward with...
Hybrid classification model is currently an active research area and successfully solves classification problems in credit scoring. Finding effective classificatory models is important. Classification in credit scoring has been regarded as a critical topic, with its related departments collecting huge amounts of data to avoid making the wrong decision. Filter feature selection model is important in...
A Multi-relational Bayesian Classification Algorithm with Rough Set is proposed in this paper. The concept of relational graph used to dynamic choice associative table associated with the target table, and a tuple ID propagation approach is used to solve directly the association rule mining problem with multiple database relations, and the concept of Core in Rough Set is introduced, simplify the associative...
According to the problem that K-Means clustering algorithm fails to correctly distinguish non-convex shape clusters, computation mode of distance in the algorithm is changed and density metric mode which can reflect the characteristics of data themselves is adopted instead. In the mode, Delaunay triangulation graph which has the advantages of nearest neighbour and adjacency is introduced to compute...
The system of arms information extraction based on the ontology, consists of two parts: knowledge base, processing program. It realizes the arms category determination based on text categorization, and realizes the arms object determination based on named entity recognition. It realizes the information extraction according to information extraction rules based on syntax and semantic constraint. It...
Hepatitis patients are those who need continuous special medical treatment to reduce mortality rate. Using clinical test findings data and machine learning technology such as Support Vector Machines (SVM), the classification and prediction of their life prognosis can be done. However, we cannot pledge that all the features values in the data are correlated to each other. Therefore, we incorporate...
Classification on noisy data streams has recently become one of the most important topics in streaming data mining. In this paper, a Classification algorithm for mining Data Streams based on Mixture Models of C4.5 and NB is proposed called CDSMM. In this algorithm, C4.5 is used as the base classifiers, the hypothesis testing method is introduced for the detection of concept drifts, and a Naïve Bayes...
To retrieve and extract the most satisfying among the library of components is important in component library management system. The general component retrieval system seldom provides information about respect of reused actually. Data mining technology provides a feasible approach to above problem. In the paper, how to use the application of classification method decision-tree-based to the component...
Most of the previous researches on sentiment analysis concentrate on the binary distinction of positive vs. negative. This paper presents the multi-class sentiment classification problem that attempt to mine the implied rating information from reviews. We use four machine learning methods and two feature selection methods to find out whether or not the multi-class sentiment classification problem...
Medical data often contains a large number of irrelevant and redundant features and a relatively small number of cases, which dramatically impact quality of diseases diagnosis. Hence, in quest for higher differentiation quality, feature selection is expected to improve differentiation performance. In this paper, we describe a heuristic approach based on Rough Sets theory and information theory, for...
Traditional machine learning and data mining algorithms mainly assume that the training and test data must be in the same feature space and follow the same distribution. However, in real applications, these two hypotheses are difficult to hold, traditional algorithms are hence no longer applicable. As a new framework of learning, transfer learning could solve this problem effectively. This paper focuses...
Uncertainty is the intrinsic property of spatial data and one of important factors affecting the course of spatial data mining. There are diversiform forms for the essentiality and aspect of uncertainty in the spatial objects of geographic information system. Essentiality of uncertainty may consist of the components of randomicity, fuzzy, chaos, etc. And the latter, i.e. aspect of uncertainty, may...
Customers are resources of the enterprises' profits, Customer satisfaction degree is defined as a measure of how a firm's product or service performs compared to customer's expectations. With the market developing quickly, how to improve the customers' satisfaction degree has become the main task and object for one company. It has been a subject of research due to its importance for measuring marketing...
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