Frequent pattern discovery over data stream is a hard problem because a continuously generated nature of stream does not allow a revisit on each data element. Furthermore, pattern discovery process must be fast to produce timely results. Based on these requirements, we propose an approximate approach to tackle the problem of discovering frequent patterns over continuous stream. Our approximatio...

We discuss the frequent pattern mining problem in a general setting. From an analysis of abstract representations, summarization and frequent pattern mining, we arrive at a generalization of the problem. Then, we show how the problem can be cast into the powerful language of algorithmic information theory. This allows us to formulate a simple algorithm to mine for all frequent patterns. 1

Transformation-based learning (TBL) and frequent pattern discovery (FPD) are two popular research paradigms, one from the domain of empirical natural language processing , the second from the eld of data mining. This paper describes how Eric Brill's original TBL algorithm can be improved via incorporation of FPD techniques. The algorithm B-Warmr is presented that upgrades TBL to rst-order logic...

Data mining is the process of exploring and analyzing data from different perspective, using automatic or semiautomatic techniques to extract knowledge or useful information and discover correlations or meaningful patterns and rules from large databases. One of the most vital characteristic missed by the traditional data mining systems is their capability to record and process time-varying aspe...

Taishin Kin4 Kiyoshi Asai4,5 [email protected] [email protected] 1 Mizuho Information & Research Institute, Inc, 2-3 Kanda-Nishikicho, Chiyoda-ku, Tokyo, Japan 2 Max Planck Institute for Biological Cybernetics, Spemannstr. 38, 72076 Tübingen, Germany 3 Google Japan, Inc, 26-1 Sakuracho, Shibuya, Tokyo, Japan 4 National Institute of Advanced Industrial Science and Technology (AIST), 2-43...