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IPv6 will inevitably take the place of IPv4 as the next generation of the Internet Protocol. Despite IPv6 has better security than IPv4, but there still have some security issues. So it is an urgent problem to requirement of IDS for IPv6 networks. Many intelligent information processing methods, data mining technology and so on have been applied to improve detection accuracy for IPv4 network. At first...
Association mining aims to find valid correlations among data attributes, and has been widely applied to many areas of data analysis. In this paper we present a semantic network based association analysis model including three spreading activation methods, and apply this model to assess the quality of a dataset, and generate semantically valid new hypotheses for further investigation. We evaluate...
Association rule extraction is a widely used exploratory technique which has been exploited in different contexts (e.g., biological data, medical images). However, association rule extraction, driven by support and confidence constraints, entails (i) generating a huge number of rules which are difficult to analyze, or (ii) pruning rare itemsets, even if their hidden knowledge might be relevant. To...
Intervention analysis is the common method to reveal relationships between objects in human as well as biological society. Data mining research community is just starting to pay attention to intervention analysis. As the traditional association rules are not successful at measuring intervention, this paper tries to mining intervention rules from time series data of sub-complex system. The main contributions...
In the paper, we discussed the characteristics of data mining on association rules for multi-dimension data. Then through the multi-dimension data attributes analysis and OLAP operations, we integrate the OLAP and data mining based on their advantages to one method which is called On-Line Analysis Mining (OLAM). Based on OLAM, an algorithm for multi-dimension data on association rules has been reformed...
Recently Negative Association Rule Mining (NARM) has become a focus in the field of spatial data mining. Negative association rules are useful in data analysis to identify objects that conflict with each other or that complement each other. Much effort has been devoted for developing algorithms for efficiently discovering relation between objects in space. All the traditional association rule mining...
Today, electronic prescription in clinical scenarios is frequently used to support decision-making tasks and services monitoring in public health institutions. The huge amount of data that has been stored during last years about medication prescriptions makes now possible to apply data mining techniques with a significant success to discover patterns that could be useful to optimize medical services...
Data analysis has become an integral part in many economic fields. In this paper, we present several real-world applications occurring in the fields of automobile development and manufacturing, finance, and online communities. The given examples share one aspect in common: time. It is not only the fact to find patterns inside data volumes but also to identify them based on their temporal behaviour...
After introduce briefly about the basic concept and methods of association rules in data mining, we take borrowing records in library as example to describe the whole process of how to find association with the application of Apriori algorithm in Clementine. The mining outcome shows that some books and books have stronger association strength. According to the association between books, we can do...
Segmentation aims to separate homogeneous areas from the sequential data, and plays a central role in data mining. It has applications ranging from finance to molecular biology, where bioinformatics tasks such as genome data analysis are active application fields. In this paper, we present a novel application of segmentation in locating genomic regions with coexpressed genes. We aim at automated discovery...
Software defects are the key factors to evaluate the dependable software. This paper analyzes the attributes of software defects, and applies positive and negative association rules method to the research of software defects. This method can not only overcome the weak point of the traditional association rules method that can only mine the explicit rules, but also output some more meaningful rules...
In this paper, we examine issues related to the research and applications of computational intelligence techniques in security data analysis. We focus on solve problems that involve incomplete, vague or uncertain information, which is difficult to come to a crisp solution. It is shown how an extended mass assignment framework can be used to extract relations between soft categories. These relations...
Association rules mining methods have been recently applied to gene expression data analysis to reveal relationships between genes and different conditions and features. However, not much effort has focused on detecting the relation between gene expression maps and related gene functions. Here we describe such an approach to mine association rules among gene functions in clusters of similar gene expression...
Aimed at the limitation of the current FUP algorithm, which readily led to the low efficiency of frequent itemset of the updated database, a novel association rules mining algorithm named QAIS which is different from the classical dual phrase was proposed. On the basis of QAIS, an improved association rules mining algorithm named AIU was put forward further. AIU can efficiently maintain association...
Apriori is a well-known algorithm which is used extensively in market-basket analysis and data mining. The algorithm is used for learning association rules from transactional databases and is based on simple counting procedures. In this paper we propose enhancements to Apriori which allow it to perform concept classification similar to the way decision tree algorithms learn. Specifically, training...
This paper aims to propose a weighted linguistic associative classification model for uncertainty data analysis using rough membership function. Transformation of quantitative association rules into linguistic representation can be achieved in discretizing the numerical interval into rough interval described with respective rough membership values. Transformation of linguistic information system is...
The high dependability software is not only one of software technique development commanding points, but also is the software industry development essential foundation, this paper summarizes the data mining to face the detect of the software credibility test, the appraisal and the technical aspect newest research, elaborated the data mining technology in the software flaw test application, including...
Temporal data mining is becoming an important tool for health care providers and decision makers. The capability of handling and analyzing complex multivariate data may allow to extract useful information coming from the day-by-day activity of health care organizations as well as from patients monitoring. In this paper we review the main approaches presented in the literature to mine biomedical time...
In this paper, we describe a set-based approach for mining association rules and finding frequent sequential patterns in customer transactional databases. The set-based approach is a direct improvement of the original association rule mining algorithms proposed by R. Agrawal and R. Skrikant. Our approach relaxes the constraints described in Apriori (All/Some), and improves the performance while being...
This paper proposes a method of EMD (extraction method of distributed heterogeneous dataset in multi-support association rule mining) which can be applied into filtering, abstraction, analysis and transformation of data feature record set in multi-support association rule mining. In order to ensure the efficient implementation of multi-support association rule mining, the format of data feature record...
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