Knowledge Discovery in Textual Databases: A Concept-Association Mining Approach

Abstract

Concepts are often related to short sequences of words that occur frequently together across the text collections. Such concepts convey much of the meaning in any language. Association rule mining is a powerful technique for extracting relations among concepts. From a text mining perspective, association rules have mainly been used in a traditional support-confidence framework. This approach suffers from a tendency to generate a prohibitively large number of candidate rules. Recent works try to eliminate irrelevant rules from the result set. Recently also, the measures of bond and all-confidence were proposed instead of the more traditional support and confidence framework.This chapter describes a new approach for mining associations among concepts from text collections. Our goal is to extract interesting associations among concepts from their co-occurrences within text collections. The proposed approach consists of three steps. We first extract the concepts themselves from the texts. Then we extract interesting associations among the concepts using association rule mining. Finally we construct a directed graph from the mined associations.

What Causes the Virtual Agglomeration of Creative Industries?

“…Qualitative methods are not entirely suitable for this research. With the ongoing advancements in bigdata-related research, quantitative methods based on big data web crawling and text mining can provide excellent solutions [45,46], and researchers are increasingly obtaining results that better reflect reality using big data text mining methods [47][48][49]. Taking the Shenzhen Dafen Oil Painting Village as an example, Yuan et al [50] studied the convergence between creative industry parks and tourism using ROST Content Mining 6 and performing an analysis based on the network text.…”