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To extract implicit knowledge and data relationships from the audio and audio similarity measure, this paper uses the audio mining techniques. A model for audio clustering and classification technique is proposed. Neural networks are used for classifying the data. The working prototype of the Music classification system has been developed and tested in MATLAB 6.5 using the signal Processing Toolbox...
Extracting useful information from user generated text on the web is an important ongoing research in natural language processing, machine learning, and data mining. Online tools like emails, news groups, blogs, and web forums provide an effective communication platform for millions of users around the globe and also provide an added advantage of anonymity. Millions of people post information on different...
The popularity of the Internet has caused a massive increase in the amount of Web pages. The information explosion has led to a growing challenge for information retrieval systems. Document clustering becomes an important process for helping the information retrieval systems organize this vast amount of data. It is believed that grouping similar documents together into clusters will help the users...
In this paper, we describe a two-stage hybrid approach to select gene features and produce dominant patterns for evaluating the pathological probability. To discover suitable genes as experiment samples for distinguishing the status of gene regulation, we utilized receiver operating characteristic (ROC) method to eliminate non-significant genes of unapparent variation between normal tissues and tumors...
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...
The 3 most important issues for anomaly detection based intrusion detection systems by using data mining methods are: feature selection, data value normalization, and the choice of data mining algorithms. In this paper, we study primarily the feature selection of network traffic and its impact on the detection rates. We use KDD CUP 1999 dataset as the sample for the study. We group the features of...
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