scholarly journals A New Approach of Multi-Robot Cooperative Pursuit Based on Association Rule Data Mining

10.5772/7255 ◽  
2009 ◽  
Vol 6 (4) ◽  
pp. 33 ◽  
Author(s):  
Jun Li ◽  
Qishu Pan ◽  
Bingrong Hong
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
New Approach ◽  
Multi Robot

scholarly journals Using the interestingness measure lift to generate association rules

2015 ◽  
Vol 4 (1) ◽  
pp. 156 ◽  
Author(s):  
Nada Hussein ◽  
Abdallah Alashqur ◽  
Bilal Sowan
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Search Space ◽  
The Other ◽  
Frequent Patterns ◽  
New Approach ◽  
Left Hand ◽  
Interestingness Measure ◽  
The Right

<p>In this digital age, organizations have to deal with huge amounts of data, sometimes called Big Data. In recent years, the volume of data has increased substantially. Consequently, finding efficient and automated techniques for discovering useful patterns and relationships in the data becomes very important. In data mining, patterns and relationships can be represented in the form of association rules. Current techniques for discovering association rules rely on measures such as support for finding frequent patterns and confidence for finding association rules. A shortcoming of confidence is that it does not capture the correlation that exists between the left-hand side (LHS) and the right-hand side (RHS) of an association rule. On the other hand, the interestingness measure lift captures such as correlation in the sense that it tells us whether the LHS influences the RHS positively or negatively. Therefore, using Lift instead of confidence as a criteria for discovering association rules can be more effective. It also gives the user more choices in determining the kind of association rules to be discovered. This in turn helps to narrow down the search space and consequently, improves performance. In this paper, we describe a new approach for discovering association rules that is based on Lift and not based on confidence.</p>

scholarly journals POST-MINING OF ASSOCIATION RULES USING ONTOLOGIES AND RULE SCHEMAS

2015 ◽  
pp. 196-200
Author(s):  
R. SUBASH CHADRA BOSE ◽  
R. SIVAKUMAR
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Post Processing ◽  
Rule Mining ◽  
New Approach ◽  
Useful Knowledge ◽  
General Terms ◽  
User Knowledge

Knowledge discovery and databases (KDD) deals with the overall process of discovering useful knowledge from data. Data mining is a particular step in this process by applying specific algorithms for extracting hidden fact in the data. Association rule mining is one of the data mining techniques that generate a large number of rules. Several methods have been proposed in the literature to filter and prune the discovered rules to obtain only interesting rules in order to help the decision-maker in a business process. We propose a new approach to integrate user knowledge using ontologies and rule schemas at the stage of post-mining of association rules. General Terms- Lattice, Post-processing, pruning, itemset .

scholarly journals Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis

2021 ◽  
Vol 11 (4) ◽  
pp. 1715
Author(s):  
Jieh-Ren Chang ◽  
You-Shyang Chen ◽  
Chien-Ku Lin ◽  
Ming-Fu Cheng
Keyword(s):  
Data Mining ◽  
Customer Service ◽  
Flash Memory ◽  
Association Rule ◽  
Production Control ◽  
Frequent Pattern ◽  
Nand Flash ◽  
Growth Data ◽  
Nand Flash Memory ◽  
Service Strategy

Storage devices in the computer industry have gradually transformed from the hard disk drive (HDD) to the solid-state drive (SSD), of which the key component is error correction in not-and (NAND) flash memory. While NAND flash memory is under development, it is still limited by the “program and erase” cycle (PE cycle). Therefore, the improvement of quality and the formulation of customer service strategy are topics worthy of discussion at this stage. This study is based on computer company A as the research object and collects more than 8000 items of SSD error data of its customers, which are then calculated with data mining and frequent pattern growth (FP-Growth) of the association rule algorithm to identify the association rule of errors by setting the minimum support degree of 90 and the minimum trust degree of 10 as the threshold. According to the rules, three improvement strategies of production control are suggested: (1) use of the association rule to speed up the judgment of the SSD error condition by customer service personnel, (2) a quality strategy, and (3) a customer service strategy.

New Approach of Data Mining from the Complex Impedance Plane: Parameters You Have Always Wanted to Have

2014 ◽  
Vol 61 (1) ◽  
pp. 217-221
Author(s):  
J. M. Macak ◽  
D. Patil ◽  
M. Fraenkl ◽  
V. Zima ◽  
K. Shimakawa ◽  
...  
Keyword(s):  
Data Mining ◽  
Complex Impedance ◽  
New Approach ◽  
Impedance Plane

scholarly journals Association rule hiding using integer linear programming

2021 ◽  
Vol 11 (4) ◽  
pp. 3451
Author(s):  
Suma B. ◽  
Shobha G.
Keyword(s):  
Data Mining ◽  
Linear Programming ◽  
Integer Linear Programming ◽  
Association Rule ◽  
Real Life ◽  
Linear Program ◽  
Integer Linear Program ◽  
Optimization Approach ◽  
Integer Linear Program Formulation ◽  
The Impact

<span>Privacy preserving data mining has become the focus of attention of government statistical agencies and database security research community who are concerned with preventing privacy disclosure during data mining. Repositories of large datasets include sensitive rules that need to be concealed from unauthorized access. Hence, association rule hiding emerged as one of the powerful techniques for hiding sensitive knowledge that exists in data before it is published. In this paper, we present a constraint-based optimization approach for hiding a set of sensitive association rules, using a well-structured integer linear program formulation. The proposed approach reduces the database sanitization problem to an instance of the integer linear programming problem. The solution of the integer linear program determines the transactions that need to be sanitized in order to conceal the sensitive rules while minimizing the impact of sanitization on the non-sensitive rules. We also present a heuristic sanitization algorithm that performs hiding by reducing the support or the confidence of the sensitive rules. The results of the experimental evaluation of the proposed approach on real-life datasets indicate the promising performance of the approach in terms of side effects on the original database.</span>

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