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A decision support system based on data mining (DM) and Bayesian belief networks (BBN) is proposed to predict the student learning outcomes and takes the calculus course as an example to help students overcome their learning difficulties. Total of 427 freshmen in Ming Chi University of Technology (Taiwan) did questionnaires to assist this study. The methodologies involves four steps: fuzzy theory...
We propose an Association Rule Mining (ARM) based Recommender system for the stock markets. Normally technical and fundamental analyses are a basis of prediction of stock price. Several systems exist for monitoring and prediction of stock prices. But these deal with individual stocks. They do not give the inter-relationship between stocks or their relationship with the stock market INDEX. Our method...
Association rule mining is sought for items through a fairly large data set relation are certainly consequential. The traditional association mining based on a uniform minimum support, either missed interesting patterns of low support or suffered from the bottleneck of item set generation. An alternative solution relies on exploiting support constraints which specifies the required minimum support...
Mining association rules is one of the most important tasks in data mining. Several approaches generalizing association rules to fuzzy association rules have been proposed. In this paper we present a general framework for mining fuzzy association rule. Based on apriori algorithm, a new algorithm for mining fuzzy association rules is proposed. Experimental results illustrate the algorithm is more effective.
In data mining approach, the quantitative attributes should be appropriately dealt with as well as the Boolean attributes. This paper presents a fast algorithm for extracting fuzzy association rules from database. The objective of the algorithm is to improve the computational time of mining for the actual application. In this paper, we propose a basic algorithm based on the Apriori algorithm for rule...
In this paper, an enhanced efficient approach for speeding up the evolution process for finding minimum supports, membership functions and fuzzy association rules is proposed by utilizing clustering techniques. All the chromosomes use the requirement satisfaction derived only from the representative chromosomes in the clusters and from their own suitability of membership functions to calculate the...
There are two shortages in usual methods for agricultural land evaluation: (1) too many manual interferences into the calculating procession, (2) the relatively large differences of partial units are concealed in certain factors. We designed a hyper graph clustering model in this paper based on fuzzy frequent item sets to conduct the evaluation for quality of agricultural land. The database for land...
With the rapid development of computer network technology, network not only provides the service for the people, but also has brought many negative effects. Intrusion detection is used to solve this problem. In order to improve the speed and intensity of intrusion detection, data mining technology can be applied to intrusion detection systems. Association rules are a common method in data mining....
This paper presents an online real-time network response system, which can determine whether a LAN is suffering from a flooding attack within a very short time unit. The detection engine of the system is based on the incremental mining of fuzzy association rules from network packets, in which membership functions of fuzzy variables are optimized by a genetic algorithm. The proposed online system belongs...
The paper deals with the following topics: granular computing; data mining; fuzzy logic; information systems; Internet; fuzzy set theory; rough set theory; image processing; association rules; data security; Web services; cryptography; mobile computing; and support vector machines.
In this article, we have introduced some genetic-fuzzy data mining techniques and their classification. The concept of fuzzy sets is used to handle quantitative transactions and the process of genetic calculation is executed to find appropriate membership functions. The main contributions of this paper are that we first divided the genetic-fuzzy mining problems into four kinds according to the types...
In this article, we have introduced some genetic-fuzzy data mining techniques and their classification. The concept of fuzzy sets is used to handle quantitative transactions and the process of genetic calculation is executed to find appropriate membership functions. The main contributions of this paper are that we first divided the genetic-fuzzy mining problems into four kinds according to the types...
According to the existing mining algorithm of fuzzy association rules, a novel fuzzy positive and negative association rules algorithm will be proposed in this paper. We focus on the membership function of fuzzy set and minimum support parameters of positive and negative association rules and adopt a method that selects parameters automatically which is based on the k-means clustering. Besides, multi-level...
To address the problems of the rule redundancy and the long algorithm execution time in the process of mining one airborne radar intelligence database by the fuzzy association rules algorithm, this paper define a new QL-implicator based fuzzy support measure in order to enhance the recognition probability of the positive association rules and introduce the fuzzy conditional entropy measure (CE-measure)...
The activities and decisions of organizations and companies are based on data and the information obtained from data analysis. Data quality plays a crucial role in data analysis, because the incorrect data leads to wrong decisions. Nowadays, improving the data quality manually is very difficult and in many cases is impossible as data quality is one of the complicated and non-structured concepts and...
Presently, in the data mining scenario clustering of large dataset is one of the very important techniques widely applied to many applications including social network analysis. Applying more specific pre-processing method to prepare the data for clustering algorithms is considered to be a significant step for generating meaningful segments. In this paper we propose an innovative clustering technique...
Classification is one of the most popular data mining techniques applied to many scientific and industrial problems. Recently, fuzzy association rule has been extensively studied in classification. In this paper, a new classification model is proposed, which is based on interpretable fuzzy association rules and automatic generating membership functions. In addition,a modified algorithm for classification...
To improve the intelligibility and efficiency of knowledge expression for the land evaluation, a land evaluation method combining simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. To reduce the complexity of the land evaluation models and improve the efficiency and intelligibility of fuzzy classification association rules further, an algorithm to eliminate...
Discovery the association between web pages is an important task as the rapid growth of web data. This article uses the fuzzy method to discover generalized fuzzy association rules among theWeb pages fromWeb logs. In the paper, whether a web page is visited or not and time duration on it are considered two important factors to reflect users' interest and preference. Numerical time duration is fuzzified...
An effective tracking method is proposed to solve the problem that the electro-optical tracking system in Missile Range easily loses the real target during the target separation. Before target separation, the error correcting value of the theoretical trajectory is obtained by the theoretical trajectory correcting algorithm. In the phase of target separation, the theoretical trajectory of the target...
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