Mining related queries from Web search engine query logs using an improved association rule mining model

2007 ◽  
Vol 58 (12) ◽  
pp. 1871-1883 ◽  
Author(s):  
Xiaodong Shi ◽  
Christopher C. Yang
Keyword(s):  
Search Engine ◽  
Association Rule ◽  
Association Rule Mining ◽  
Web Search ◽  
Rule Mining ◽  
Web Search Engine ◽  
Query Logs ◽  
Mining Model

Mining for Mutually Exclusive Items in Transaction Databases

2008 ◽  
pp. 2993-3004
Author(s):  
George Tzanis ◽  
Christos Berberidis
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
The Other ◽  
Review Of The Literature ◽  
Rule Mining ◽  
Transaction Data ◽  
Mining Algorithm ◽  
Concise Review ◽  
Evaluation Metric ◽  
Mining Model

Association rule mining is a popular task that involves the discovery of co-occurences of items in transaction databases. Several extensions of the traditional association rule mining model have been proposed so far; however, the problem of mining for mutually exclusive items has not been directly tackled yet. Such information could be useful in various cases (e.g., when the expression of a gene excludes the expression of another), or it can be used as a serious hint in order to reveal inherent taxonomical information. In this article, we address the problem of mining pairs of items, such that the presence of one excludes the other. First, we provide a concise review of the literature, then we define this problem, we propose a probability-based evaluation metric, and finally a mining algorithm that we test on transaction data.

scholarly journals Construction Search Engine Based on Formal Concept Analysis and Association Rule Mining

2012 ◽  
Vol 6-7 ◽  
pp. 625-630 ◽  
Author(s):  
Hong Sheng Xu
Keyword(s):  
Search Engine ◽  
Association Rule ◽  
Formal Concept Analysis ◽  
Association Rule Mining ◽  
High Efficiency ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Frequent Itemsets ◽  
Formal Concept ◽  
Rule Mining

In the form of background in the form of concept partial relation to the corresponding concept lattice, concept lattice is the core data structure of formal concept analysis. Association rule mining process includes two phases: first find all the frequent itemsets in data collection, Second it is by these frequent itemsets to generate association rules. This paper analyzes the association rule mining algorithms, such as Apriori and FP-Growth. The paper presents the construction search engine based on formal concept analysis and association rule mining. Experimental results show that the proposed algorithm has high efficiency.

Prediction of the Next Question for the Question Answering System

2016 ◽  
Vol 5 (2) ◽  
pp. 51
Author(s):  
Manvi Breja
Keyword(s):  
Search Engine ◽  
Association Rule ◽  
Association Rule Mining ◽  
Question Answering ◽  
User Profile ◽  
User Profiling ◽  
Rule Mining ◽  
Question Answering System ◽  
Novel Approach ◽  
Initial Question

<span>User profiling, one of the main issue faced while implementing the efficient question answering system, in which the user profile is made, containing the data posed by the user, capturing their domain of interest. The paper presents the method of predicting the next related questions to the first initial question provided by the user to the question answering search engine. A novel approach of the association rule mining is highlighted in which the information is extracted from the log of the previously submitted questions to the question answering search engine, using algorithms for mining association rules and predicts the set of next questions that the user will provide to the system in the next session. Using this approach, the question answering system keeps the relevant answers of the next questions in the repository for providing a speedy response to the user and thus increasing the efficiency of the system.</span>

Mining for Mutually Exclusive Items in Transaction Databases

2009 ◽  
pp. 2192-2203
Author(s):  
George Tzanis ◽  
Christos Berberidis
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
The Other ◽  
Review Of The Literature ◽  
Rule Mining ◽  
Transaction Data ◽  
Mining Algorithm ◽  
Concise Review ◽  
Evaluation Metric ◽  
Mining Model

Association rule mining is a popular task that involves the discovery of co-occurences of items in transaction databases. Several extensions of the traditional association rule mining model have been proposed so far; however, the problem of mining for mutually exclusive items has not been directly tackled yet. Such information could be useful in various cases (e.g., when the expression of a gene excludes the expression of another), or it can be used as a serious hint in order to reveal inherent taxonomical information. In this article, we address the problem of mining pairs of items, such that the presence of one excludes the other. First, we provide a concise review of the literature, then we define this problem, we propose a probability-based evaluation metric, and finally a mining algorithm that we test on transaction data.

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