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Aiming at value reduction, a kind of RSVR algorithm was presented based on support in association rules via Apriori algorithm. A more effective reduction table can be obtained by deleting those rules with less support according to least support - minsup. The reduction feasibility of this algorithm was achieved by reducing the given decision table. Testing by UCI machine learning database and comparing...
The discovery of knowledge from medical databases is important in order to make effective medical diagnosis. The aim of data mining is extract the information from database and generate clear and understandable description of patterns. In this study we have introduced a new approach to generate association rules on numeric data. We propose a modified equal width binning interval approach to discretizing...
Specification mining is a machine learning approach for discovering specifications of the protocols that code must obey when interacting with an application program interface or abstract data type. Two major concerns in engineering software systems are high maintenance costs and reliability of systems. To reduce maintenance efforts, there is a need for automated tools to help software developers understand...
Reinforcement learning is an important method of machine learning. This paper using the graph theory to express varieties of knowledge points, which their's relationship is expressed by the graph of topological graph. Applied the Technology of association rule Recommendation to deal with the relationship between these knowledge points, give the corresponding of the recommendation work flow chart....
We present a tool called BugFix that can assist developers in fixing program bugs. Our tool automatically analyzes the debugging situation at a statement and reports a prioritized list of relevant bug-fix suggestions that are likely to guide the developer to an appropriate fix at that statement. BugFix incorporates ideas from machine learning to automatically learn from new debugging situations and...
When data objects that are the subject of analysis using machine learning techniques are described by a large number of feature (i.e. the data is high dimension) it is often beneficial to reduce the dimension of the data. dimensionality reduction (DR) can be beneficial not only reasons of computational efficiency but also because it can improve the accuracy of the analysis. Now we have tried to introduce...
Recent attacks demonstrated that network intrusions have become a major threat to Internet. Systems are employed to detect internet anomaly play a vital role in Internet security. To solve this problem, a technique called frequent episode rules (FERs) base on data mining has been introduced into anomaly detection system (ADS). These episode rules are used to distinguish anomalous sequences of TCP,...
Mining association rules plays an essential role in data mining tasks. Many algorithms have been proposed for mining Boolean association rules, but they cannot deal with quantitative and categorical data directly. Although we can transform quantitative attributes into intervals and applying Boolean algorithms to the intervals. But this approach is not effective and is difficult to scale up for high-dimensional...
Automatic configuration of large and heterogeneous ICT systems and their dependability mechanisms is both desirable and daunting for the inherent complexity of these systems.Configurations are commonly designed based on personal expertise, best practice, empirical evidence, without any automatic process and formal validation mechanism. This approach leads to frequent and reiterate errors with severe...
Many results in the literature indicate that the incremental approach to association mining leads to gain regarding the time needed to obtain the rules, but there is no evaluation about their quality, compared to non-incremental algorithms. This paper presents the comparison of usage of two typical algorithms representing each approach: APriori and ZigZag. Execution time clearly shows the advantage...
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