Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
SPARTAN: a model-based semantic compression system for massive data tables
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Semantic Compression and Pattern Extraction with Fascicles
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
ItCompress: An Iterative Semantic Compression Algorithm
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
General purpose database summarization
VLDB '05 Proceedings of the 31st international conference on Very large data bases
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Major media companies such as The Financial Times, the Wall Street Journal or Reuters generate huge amounts of textual news data on a daily basis. Mining frequent patterns in this mass of information is critical for knowledge workers such as financial analysts, stock traders or economists. Using existing frequent pattern mining (FPM) algorithms for the analysis of news data is difficult because of the size and lack of structuring of the free text news content. In this article, we demonstrate a comprehensive Streaming TEmporAl Data (STEAD) analysis framework for mining frequent patterns in financial news. In this demonstration, we show how the mining task is supported by the use of a Time-Aware Content Summarization algorithm (TACS). This summary generates a concise representation of large volume of data by taking into account the expert's peculiar interest while preserving the news arrival temporal information which is essential for FPM algorithms. We experimented the whole framework on a set of news data from Reuters.