CSCR001: Literature Survey
نویسنده
چکیده
My PhD research focuses on Text Mining (TM), one major school in Knowledge Discovery in Data (KDD), and in particular the task of classification/categorization of documents using novel algorithms for the identification of hidden patterns within these documents. Two significant techniques of Data Mining (DM), another well-known major school in KDD, will be utilized to support the research: Association Rule Mining (ARM) and Classification Rule Mining (CRM). KDD is about discovering unknown knowledge from structured or unstructured data that contains various mining tasks, classification, clustering, mining association rules, etc. DM is about finding hidden rules, such as association rules, classification rules, etc., in structured database-data, whereas TM concerns about finding hidden patterns, rules, regularities and trends from non-databasedata, especially textual data (text files, web documents, etc.). ARM emphasizes on extracting the covert rules of co-occurrence among items/attributes through mining a very large binary dataset. From my previous research, I have categorized a number of common existing ARM algorithms into three “families”, and have identified four major essential issues in ARM. Currently, I am summarizing different approaches of employing ARM technique in mining textual data. In addition, my research will draw on my current work on the identification of essential issues in CRM. This work will be applied with a view to the discovery of novel hybrid ARM/CRM algorithms for TM.
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تاریخ انتشار 2004