MINING USER AWARE RARE SEQUENTIAL TOPIC PATTERNS IN DOCUMENT STREAM

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC).

Abstract

Among various multi keyword and sequential topic semantics, we choose the efficient similarity measure of “coordinate matching”, i.e., as many matches as possible, to capture the relevance of data documents to the search query. During the index construction, each document is associated with a binary vector as a sub-index where each bit represents whether corresponding keyword is contained in the document. We implement STP (Sequential Topic Pattern) which captures both combinations and orders of topics and compared to document-based patterns, topic-based patterns contain abstract information of document contents and are thus beneficial in clustering similar documents and finding some regularities about Internet users. STPs happen to be able to combine a series of inter-correlated messages, and can thus capture such behaviors and associated users. We propose a probability model that can capture the normal mentioning behavior of a user, which consists of both the number of mentions per post and the frequency of users occurring in the mentions. Then this model is used to measure the anomaly of future user behavior. Using the proposed probability model, we can quantitatively measure the novelty or possible impact of a post reflected in the mentioning behavior of the user. We aggregate the anomaly scores obtained in this way over hundreds of users and apply a recently proposed change point detection technique based on the sequentially discounting normalized maximum-likelihood coding. This technique can detect a change in the statistical dependence structure in the time series of aggregated anomaly scores, and pinpoint where the topic emergence is; the proposed approach can detect changes in the communication patterns of users even in a realistic setting when only some part of the users reacts to the emerging topic.

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Keywords

Sequential Topic Pattern, probability model, Secret Key, Search Data.