نتایج جستجو برای: fuzzy data mining
تعداد نتایج: 2515323 فیلتر نتایج به سال:
Multidimensional databases and OLAP (On Line Analytical Processing) tools provide an eÆcient framework for data mining and have led to the so-called OLAP Mining architecture ([10]). Besides, data from real world are often imperfect, either because they are uncertain, or because they are imprecise. Moreover, the use of fuzzy set theory in data mining systems enhances the understandability of the...
In this study, a system for monitoring the structural health of bridge deck and predicting various possible damages to this section was designed based on measuring the temperature and humidity with the use of wireless sensor networks, and then it was implemented and investigated. A scaled model of a conventional medium sized bridge (length of 50 meters, height of 10 meters, and with 2 piers) wa...
Background & Aim: A main problem in diabetes is its timely and accurate diagnosis. This study aimed at diagnosing diabetes using data mining methods. Methods: The present study is an analytical investigation including 768 individuals with 8 attributes. Artificial neural networks and fuzzy neural networks were used to diagnose the diabetes. To achieve a real accuracy, the Kfold method was used ...
Data Mining is most commonly used in attempts to induce association rules from databases which can help decision-makers easily analyze the data and make good decisions regarding the domains concerned. Different studies have proposed methods for mining association rules from databases with crisp values. However, the data in many real-world applications consist of interval and fuzzy values. In th...
Extracting multilevel association rules in transaction databases is most commonly used tasks in data mining. This paper proposes a multilevel association rule mining using fuzzy concepts. This paper uses different fuzzy membership function to retrieve efficient association rules from multi level hierarchies that exist in a transaction dataset. In general, the data can spread into many hierarchi...
Different studies have proposedmethods formining fuzzy association rules fromquantitative data, where themembership functions were assumed to be known in advance. However, it is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for mining fuzzy association rules. This paper thus presents a new fuzzy data-mining algorithm for extr...
Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most conventional data-mining algorithms identify the relationships among transactions using binary values and set the minimum supports and minimum confidences at numerical values. Linguistic minimum support and minimum confidence values are, however, more natural ...
For carrying out a real-time and effective fault diagnosis to satellite, this paper studies a new data mining approach that is called the fuzzy incomplete approach, and gives its algorithm implementation in the satellite fault diagnosis. The application comparison of the new data mining approach with other data mining approaches is discussed by measuring the performance of each approach from th...
In this paper, interactions among fuzzy, rough, and soft set theory has been studied. The authors have examined these theories as a problem solving tool in association rule mining problems of data mining and knowledge discovery in databases. Although fuzzy and rough set have been well studied areas and successfully applied in association rule mining problem, but soft set theory needs more atten...
Data mining of association rules from items in transaction databases has been studied extensively in recent years. However these algorithms deal with only transactions with binary values whereas transactions with quantitative values are more commonly seen in real-world applications. As to fuzzy data mining, many approaches have also been proposed for mining fuzzy association rules. Most of the ...
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