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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...
Aim at the problem that classical Euclidean distance metric cannot generate a appropriate partition for data lying in a manifold, a genetic algorithm based clustering method using geodesic distance measure is put forward. In this study, a prototype-based genetic representation is utilized, where each chromosome is a sequence of positive integer numbers that represent the k-medoids. Additionally, a...
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...
Clustering with the agglomerative information bottleneck (aIB) algorithm suffers from the sub-optimality problem, which cannot guarantee to preserve as much relative information as possible. To handle this problem, we introduce a density connectivity chain, by which we consider not only the information between two data elements, but also the information among the neighbors of a data element. Based...
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