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The key technology to analyzing electricity data is cluster methods, of which the traditional way has already lost its agility and quality due to the increasing data volume. To this end, this paper presented an electricity data mining structure: first the higher dimensional data should be reduced to lower ones, second the reduced-dimensional results should be classified into typical usage behavior...
To efficiently mine the classification model for machine fault diagnosis based on images, a hybrid classification algorithm, which inspired by combining nonnegative matrix factorization and artificial immune system, was put forward. In the algorithm, nonnegative matrix factorization was employed for dimensionality reduction of the time-frequency spectral images. An artificial immune based classification...
This paper introduces the clustering-based sentiment analysis approach which is a new approach to sentiment analysis. By applying a TF-IDF weighting method, voting mechanism and importing term scores, an acceptable and stable clustering result can be obtained. It has competitive advantages over the two existing kinds of approaches: symbolic techniques and supervised learning methods. It is a well...
This paper proposes a face detection algorithm combined skin color detection and improved AdaBoost algorithm. First, skin regions are segmented from the detected image, and candidate face regions are obtained in terms of the statistical characteristics of human face; Then focusing on the phenomena of overfitting in training process of classical AdaBoost algorithm, this paper proposes a novel method...
Quantitative descriptions of white matter (WM) fiber shape and cortical folding patterns are important for neuroscience research. This paper presents a novel computational method for WM fiber shape pattern analysis, that is, WM fibers are clustered into five primitive shape patterns: closed `U', `M', curved line, open `U' and straight line, based on the automatic clustering of their shape features...
Word sense disambiguation (WSD) is a task of classification, where the local context is the basic features to identify the sense of ambiguous word. Most systems choose optimal local context window on empirical grounds, which is usually symmetric, the distance from the ambiguous word to both sides of the window is same, such as [-1, +1] or [-2, +2]. Is symmetric window better than asymmetric window?...
Enzymes are proteins that catalyze bio-chemical reactions in different ways and play important roles in metabolic pathways. The exponential rise in sequences of new enzymes has necessitated developing methods that accurately predict their function. To address this problem, approaches that cluster enzymes based on their sequence and structural similarity have been applied, but are known to fail for...
Inspired by complementary strategies, a hybrid supervised artificial immune classifier is put forward, which is on the basis of the clonal selection principle, and combined with the fuzzy c-means clustering (FCM) algorithm and information entropy theory. The new approach uses a weighted Euclidean distance based dissimilarity measure during all affinity evaluations. With the help of FCM clustering,...
Inspired by complementary strategies, a hybrid supervised artificial immune classifier is put forward, which is on the basis of the clonal selection principle, and combined with the Fuzzy C-Means clustering (FCM) algorithm. With the help of FCM clustering, the initial antibodies that image features of data set are extracted effectively, and then a clonal selection algorithm named CLONALG is adopted...
Recently, automatic text categorization has made rapid progress and been one of the hotspots in the information processing field. Text tendency classification is one type of text categorization, which has very important applications in information retrievals bad information identification and filtering , content security management and analysis of public opinion tendency. To aim at the important influence...
Analysis by way of the experiment, compared with ID3 algorithm, there is a large difference between SD-CA algorithm in this thesis and decision tree algorithm originated from ID3 And the classification rule is also different from the practice. It relates to the data containing middling, the proportions are all 0.5. Then, its results to classification are much more related to other attributes; some...
Decision tree algorithm is not only the important part of machine learning, but also the most widely used data mining tool. At present, there are many algorithms of generating decision tree, but when the database which we rely on exists noise, high quality knowledge is hard to obtain by ID3 algorithm. In this paper, we propose the data mining method based on second learning in case of ID3 algorithm,...
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