Mining Top-K Largest Tiles in a Data Stream

Abstract

Abstract. Large tiles in a database are itemsets with the largest area which is defined as the itemset frequency in the database multiplied by its size. Mining these large tiles is an important pattern mining problem since tiles with a large area describe a large part of the database. In this paper, we introduce the problem of mining top-k largest tiles in a data stream under the sliding window model. We propose a candidatebased approach which summarizes the data stream and produces the top-k largest tiles efficiently for moderate window size. We also propose an approximation algorithm with theoretical bounds on the error rate to cope with large size windows. In the experiments with two real-life datasets, the approximation algorithm is up to hundred times faster than the candidate-based solution and the baseline algorithms based on the state-of-the-art solutions. We also investigate an application of large tile mining in computer vision and in emerging search topics monitoring.

An Online Learning Model of Mobile User Preference Based on Context Quantification

“…Since in the data stream or time series, the new data will be added constantly, and the time complexity is very great when doing research on the data stream. So, the researcher use the sliding window to select the recent data to do some research [16]. In the mobile network, the mobile user behavior will happen constantly, so we can regard the mobile user preference as data stream or time series.…”

“…We set the size of the sliding window and the basic window is a fixed period time. The size of the sliding window is set 4,8,12,16, and the size of the basic window is set 1,2,3,4, the unit is week.…”