Ranking the Interestingness of Summaries from Data Mining Systems
We study data rn~rdng where the task is description by summarization, the representation language is generalized relations, the evaluation criteria are based on heuristic measures of interestingness, and the method for searching is the Multi-Attribute Generalization algorithm for domain generalization graphs. We present and empirically compare four heuristics for ranking the interestingness of generalized relations (or summaries). The measures are based on common measures of the diversity of a population, statistical variance, the Simpson index, and the Shannon index. All four measures rank less complex summaries (i.e., those with few tuples and/or non-ANY attributes) as most interesting. Highly ranked summaries provide a reasonable starting point for fixrther analysis of discovered knowledge.
We present a system to automatically generate RSS feeds from HTML documents that consist of time-series items with date expressions, e.g., archives of weblogs, BBSs, chats, mailing lists, site update descriptions, and event announcements. Our system extracts date expressions, performs structure analysis of a HTML document, and detects or generates titles from the document.