Rough Association Rule Mining in Text Documents for Acquiring Web User Information Needs

Abstract

It is a big challenge to apply data mining techniques for effective Web information gathering because of duplications and ambiguities of data values (e.g., terms). To provide an effective solution to this challenge, this paper first explains the relationship between association rules and rough set based decision rules. It proves that a decision pattern is a kind of closed pattern.It also presents a novel concept of rough association rules in order to improve the effectiveness of association rule mining. The premise of a rough association rule consists of a set of terms and a frequency distribution of terms. The distinct advantage of rough association rules is that they contain more specific information than normal association rules. It is also feasible to update rough association rules dynamically to produce effective results.

Association Rules for Knowledge Discovery From E-News Articles: A Review of Apriori and FP-Growth Algorithms

“…Data Mining (DM) is the process of extracting, exploring, and analysing large blocks of information to gather meaningful patterns and trends that can consider knowledge in the data sources. Applying DM techniques to extract interesting and useful knowledge is challenging because of the information overloading, higher duplication rates, and ambiguities of data [5]. DM in the text is known as Text Mining (TM) and is generally used to extract meaningful patterns in a collection of text data using Natural Language Processing (NLP) techniques such as Information Extraction (IE) to transform unstructured data into a structured format [6,7].…”

“…DM in the text is known as Text Mining (TM) and is generally used to extract meaningful patterns in a collection of text data using Natural Language Processing (NLP) techniques such as Information Extraction (IE) to transform unstructured data into a structured format [6,7]. One of the main operations in TM is frequent itemset mining for identifying frequent patterns, hidden patterns, themes, and the context in large datasets that are very easy to understand and interpret by data analysts and normal users [1][2][3][4][5]. Frequent itemset mining plays an essential role in many data mining tasks.…”

“…Extracting, exploring, and analyzing information to discover meaningful patterns and trends in data is the main focus of DM approaches. Even though the DM supports different techniques such as ARM, frequent itemset mining, and pattern mining, applying those techniques for the extraction of interesting and useful knowledge is challenging because of the information overloading, higher duplication rates, and ambiguities of data [5]. The most common issues in DM are handling many patterns, finding relationships between keywords, and generating knowledge using keyword relationships.…”

“…Text Mining or Document Mining is an emerging field of research due to the need to retrieve important non-trivial information or knowledge and features by transforming a collection of unstructured text data into a structured format [3,5,6]. Data Mining using textual data, also known as Text Mining and is generally used to identify meaningful patterns in a collection of text data using Natural Language Processing (NLP) techniques such as Information Extraction (IE) to transform unstructured data into a structured format [6,7].…”

Ontology Based Web Mining for Information Gathering

“…In addition, we clarified the relation between association mining and granule mining, and proved that a granule is a sort of closed pattern in [38]. We also evaluated the evolution method for rough association mining and that can obtain a significant performance (see [37]).…”