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This paper describes a model that discovers association rules from a medical database to help doctors treat and diagnose a group of patients who show similar prehistoric medical symptoms. The proposed data mining procedure consists of two modules. The first is a clustering module that is based on a neural network, Adaptive Resonance Theory 2 (ART2), which performs affinity grouping tasks on a large...
Mining generalized association rules with fuzzy taxonomic structures has been recognized as a important extension of generalized associations mining problem. To date most work on this problem, however, required the taxonomies to be static, ignoring the fact that the taxonomies of items cannot necessarily be kept unchanged. For instance, some items may be reclassified from one hierarchy tree to another...
In this paper, we adopt a more sophisticated multi-objective approach, SPEA2, to find appropriate sets of membership functions for fuzzy data mining. Two objective functions are used to find the Pareto front. The first one is to minimize the suitability of membership functions and the second one is to maximize the total number of large 1-itemsets. An experimental comparison with the previous approach...
It is important for Web search engine providers to study user behaviour in order to have a better understanding of how customers interact with search engines so that they can improve users' overall search experience. However, user behaviour in a search engine is complicated and affected by various factors, e.g. query length, intention/context/time when queries are submitted, etc. It is interesting...
Group-by is a core database operation that is used extensively in data analysis and decision support systems. In many application scenarios, it appears useful to group values according to their compliance with a certain concept instead of founding the grouping on value equality. In this paper, we propose a new SQLf construct that supports fuzzy-partition-based group-by (FGB). We show that FGB can...
Data mining is a very active and rapidly growing research area in the field of computer science. Its goal is to obtain useful knowledge for users from a database. Association rule mining from a database is one of the most well-known data mining techniques. In general, a large number of if-then rules are extracted by specifying minimum support and confidence levels. They are, however, too complicated...
Reliability of the Fuzzy Association Rules (FARs) extraction is a challenging research in knowledge discovery and data mining. Reliability refers to the trade-off between the prediction accuracy and the rules diversity. In this paper, an approach called Diverse Fuzzy Rule Base (DFRB) is proposed to extract the FARs which are used later to predict the future values. This approach also aims to ensure...
Fuzzy gradual rules of the form the more X is A, the more Y is B linguistically express information about the correlation between attributes and their co-variation. They thus provide valuable information summarizing the trends observed in a given data set. In this paper, we consider strengthened fuzzy gradual rules, i.e. gradual rules enriched with a clause introduced by the expression “all the more”:...
The current approach to postmarketing surveillance primarily relies on spontaneous reporting. It is a passive surveillance system and limited by gross underreporting (<;;10% reporting rate), latency, and inconsistent reporting. We propose a new interestingness measure, causal-leverage, to signal potential adverse drug reactions (ADRs) from electronic health databases which are readily available...
In this paper, we propose a two-phase fuzzy mining approach based on a tree structure to discover fuzzy frequent itemsets from a quantitative database. A simple tree structure called the upper-bound fuzzy frequent-pattern tree (abbreviated as UBFFP tree) is designed to help achieve the purpose. The two-phase fuzzy mining approach can easily derive the upper-bound fuzzy supports of itemsets through...
An e-Government portal is responsible to provide news, information and services to citizens, merchants and tourists in a reliable way. Previously, we proposed a Web Monitoring System (WMS) that monitors the performance of a web portal in realtime. Web portal structure is complex to analyze though it is important to discover useful patterns, evaluate performance, locate anomalies and identify problems...
Relaying on early effort estimation to predict the required number of resources is not often sufficient, and could lead to under or over estimation. It is widely acknowledge that that software development process should be refined regularly and that software prediction made at early stage of software development is yet kind of guesses. Even good predictions are not sufficient with inherent uncertainty...
Through import generalized fuzzy sets in data mining, use generalized fuzzy sets, support and confidence of association rules, put forward the concept left support, right support and orexis degree, give generalized association rules, improve Apriori algorithm, and then under generalized association rules orexis-based, not only can able to mine positive true association rules, but also negative false...
This paper presents a new method of mining weighted association rules, which can hold the “weighted downward closed property” by using an improved model of weighted support measurements in the weighted setting. Compared to some generalized weighted association rules mining, it proves that the method can quickly and efficiently mine important association rules.
The development of power system in developing countries can be affected by a great many parameters, hence it is necessary to identify the dominant factors, which are the most influential and typical. In this paper, 27 different variables were selected from statistics across China covering the period 1978-2008. By applying recently developed data mining techniques, interesting relationship between...
The paper introduces the conception of significance and presents a new algorithm -a fuzzy weight algorithm with multiple supports for mining association rules, which is based on fuzzy-comprehensive evaluation and the algorithm of multiple minimum supports. The new algorithm takes support and significance into consideration at the same time when large itemsets are produced, making the filter criterion...
Many algorithms have been proposed for mining fuzzy association rules in static datasets with quantitative attributes. However, there is few study on mining fuzzy association rules in data streams. This paper presents an algorithm FFI_Stream for fuzzy association rules mining in data streams. Efficient techniques are presented to find fuzzy association rules in data streams using time based sliding...
The learning of Fuzzy Rule-Based Classification Systems for High-Dimensional problems suffers from exponential growth of the fuzzy rule search space when the number of patterns and/or variables becomes high. In this work, we propose a fuzzy association rule-based classification method with genetic rule selection for high-dimensional problems to obtain an accurate and compact fuzzy rule-based classifier...
In order to improve boiler efficiency and reduce NOx emissions of a coal-fired boiler, a new solution strategy of combustion optimization is proposed is this paper. The key point of combustion optimization is the optimal setpoints of fuel and air parameters. As the development of electric industry, large amounts of history data are accumulated and data mining technique is applied to find some useful...
Warehouse management plays an important role in every manufacturing company. The inventory provides a buffer for the production plants. The Put-away process is one of the key activities in warehouse operations and has a significant impact on the overall performance of the warehouse. The put-away operation, if done effectively by the warehouse operators, helps the production plant to run smoothly....
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