The Rough Set-Based Algorithm for Two Steps

[[abstract]]The previous research in mining association rules pays no attention to finding rules from imprecise data, and the traditional data mining cannot solve the multi-policy-making problem. urthermore, in this research, we incorporate association rules with rough sets and promote a new point of view in applications. The new approach can be applied for finding association rules, which has the ability to handle uncertainty combined with rough set theory. In the research, first, we provide new algorithms modified from Apriori algorithm and then give an illustrative example. Finally, give some suggestion based on knowledge management as a reference for future research.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙

A Relative Tolerance Relation of Rough Set (RTRS) for potential fish yields in Indonesia

The sea is essential to life on earth, including regulating the climate, producing oxygen, providing medicines, providing habitats for marine animals, and feeding millions of people. It must be ensured that the sea continues to meet the needs of life without sacrificing the people of future generations. The sea regulates the planet’s climate and is a significant source of nutrients. The sea becomes an essential part of global commerce, while the contents of the ocean become the solution of human energy needs today and the future. The wealth and potential of the sea as a source of energy for humans today and the future needs to be mapped and described to provide a picture of marine potential to all concerned. As part of the government, the Ministry of Marine Affairs and Fisheries is responsible for the process of formulating, determining, and implementing policies in the field of marine and fisheries based on the results of mapping and extracting information from existing conditions. The results of this information can be used to predict the marine potential in a marine area. This prediction process can be developed using data-mining techniques such as applying the association rule by looking at the relationship between the quantity of fish based on the plankton abundance index. However, this association rules data-mining techniques that require complete data, which are data sets with no missing values to generate interesting rules for detection systems. The problem is often that required marine data are not available or that marine data are available, but they contain incomplete data. To address this problem, this paper introduces a Relative Tolerance Relation of Rough Set (RTRS). Novelty RTRS differs from previous rough approaches that use tolerance relationships, nonsymmetric equation relationships, and limited tolerance relationships. The RTRS approach is based on a limited tolerance relationship considering the relative precision between two objects; therefore, this is the first job to use relative precision. In addition, this paper presents the mathematical approach of the RTRS and compares it with other existing approaches using the marine real dataset to classify the marine potential level of the region. The results show that the proposed approach is better than the existing approach in terms of accuracy

Class Association Rules Mining based Rough Set Method

This paper investigates the mining of class association rules with rough set approach. In data mining, an association occurs between two set of elements when one element set happen together with another. A class association rule set (CARs) is a subset of association rules with classes specified as their consequences. We present an efficient algorithm for mining the finest class rule set inspired form Apriori algorithm, where the support and confidence are computed based on the elementary set of lower approximation included in the property of rough set theory. Our proposed approach has been shown very effective, where the rough set approach for class association discovery is much simpler than the classic association method.Comment: 10 pages, 2 figure

Rough Sets Clustering and Markov model for Web Access Prediction

Discovering user access patterns from web access log is increasing the importance of information to build up adaptive web server according to the individual user’s behavior. The variety of user behaviors on accessing information also grows, which has a great impact on the network utilization. In this paper, we present a rough set clustering to cluster web transactions from web access logs and using Markov model for next access prediction. Using this approach, users can effectively mine web log records to discover and predict access patterns. We perform experiments using real web trace logs collected from www.dusit.ac.th servers. In order to improve its prediction ration, the model includes a rough sets scheme in which search similarity measure to compute the similarity between two sequences using upper approximation

Interpretations of Association Rules by Granular Computing

We present interpretations for association rules. We first introduce Pawlak's method, and the corresponding algorithm of finding decision rules (a kind of association rules). We then use extended random sets to present a new algorithm of finding interesting rules. We prove that the new algorithm is faster than Pawlak's algorithm. The extended random sets are easily to include more than one criterion for determining interesting rules. We also provide two measures for dealing with uncertainties in association rules