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Visual analytics on frequent web usage patterns aims to help users to (i) analyze the data so as to discover implicit, previously unknown and potentially useful information in the form of collections of frequently visited web pages in a single session and to (ii) visually represent the discovered knowledge so as to gain insight about the data. In this paper, we propose an interactive visual analytics...
Frequent pattern mining aims discover implicit, previously unknown and potentially useful knowledge in the form of frequently co-occurring items, events, or objects. These discovered frequent patterns helps reveal interesting relationships such as consumer shopper behaviour. Existing mining algorithms mostly return a long textual list of frequent patterns to users. Such a long list may not be comprehensible...
Frequent itemsets which are quite useful in many applications always suffer from their huge number and information redundancy. Frequent closed itemsets that provide a minimal and lossless presentation of all frequent itemsets are a solution to the problem. In past years, frequent closed itemsets mining (FCIM) has been extensively studied and many effective FCIM algorithms have been proposed. CHARM...
Compared to other approaches that analyze object trajectories, we propose to detect anomalous video events at three levels considering spatiotemporal context of video objects, i.e., point anomaly, sequential anomaly, and co-occurrence anomaly. A hierarchical data mining approach is proposed to achieve this task. At each level, the frequency based analysis is performed to automatically discover regular...
Frequent itemset mining is a common data mining task for many real-life applications. The mined frequent itemsets can be served as building blocks for various patterns including association rules and frequent sequences. Many existing algorithms mine for frequent itemsets from traditional static transaction databases, in which the contents of each transaction (namely, items) are definitely known and...
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