11,569 research outputs found

    A Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to Knowledge Acquisition

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    Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a compact Fuzzy Rule-Based Expert Systems. It is also aimed at providing a platform for instant update of the knowledge-base in case new knowledge is discovered. The insight to the new approach strategies and underlining assumptions, the structure of FARME-D and its practical application in medical domain was discussed. Also, the modalities for the validation of the FARME-D approach were discussed

    A case study of predicting banking customers behaviour by using data mining

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    Data Mining (DM) is a technique that examines information stored in large database or data warehouse and find the patterns or trends in the data that are not yet known or suspected. DM techniques have been applied to a variety of different domains including Customer Relationship Management CRM). In this research, a new Customer Knowledge Management (CKM) framework based on data mining is proposed. The proposed data mining framework in this study manages relationships between banking organizations and their customers. Two typical data mining techniques - Neural Network and Association Rules - are applied to predict the behavior of customers and to increase the decision-making processes for recalling valued customers in banking industries. The experiments on the real world dataset are conducted and the different metrics are used to evaluate the performances of the two data mining models. The results indicate that the Neural Network model achieves better accuracy but takes longer time to train the model

    Application of Fuzzy Association Rule Mining for Analysing Students Academic Performance

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    This study examines the relationship between students’ preadmission academic profile and academic performance. Data sample of students in the Department of Computer Science in one of Nigeria private Universities was used. The preadmission academic profile considered includes ‘O’ level grades, University Matriculation Examination (UME) scores, and Post-UME scores. The academic performance is defined using students’ Grade Point Average (GPA) at the end of a particular session. Fuzzy Association Rule Mining (FARM) was used to identify the hidden relationships that exist between students’ pre-admission profile and academic performance. This study hopes to determine the academic profile of students who are most admitted in the session. It determines students’ performance ratings as against their pre-admission academic profile. This can serve as a predictor for admission committee to enhance the quality of the new in-take and guide for the academic advise

    arules - A Computational Environment for Mining Association Rules and Frequent Item Sets

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    Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and association rules.

    Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection

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    Currently, power distribution companies have several problems that are related to energy losses. For example, the energy used might not be billed due to illegal manipulation or a breakdown in the customer’s measurement equipment. These types of losses are called non-technical losses (NTLs), and these losses are usually greater than the losses that are due to the distribution infrastructure (technical losses). Traditionally, a large number of studies have used data mining to detect NTLs, but to the best of our knowledge, there are no studies that involve the use of a Knowledge-Based System (KBS) that is created based on the knowledge and expertise of the inspectors. In the present study, a KBS was built that is based on the knowledge and expertise of the inspectors and that uses text mining, neural networks, and statistical techniques for the detection of NTLs. Text mining, neural networks, and statistical techniques were used to extract information from samples, and this information was translated into rules, which were joined to the rules that were generated by the knowledge of the inspectors. This system was tested with real samples that were extracted from Endesa databases. Endesa is one of the most important distribution companies in Spain, and it plays an important role in international markets in both Europe and South America, having more than 73 million customers

    The Impact of Some Socio-Economic Factors on Academic Performance: A Fuzzy Mining Decision Support System

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    Due to the reported impacts of some socio-economic factors on academic performance and nations’ education value, there is need for strong awareness to assist students in making the right decision. To this effect, this study proposes and designs student decision support system for determining the extent to which different levels of some socio-economic factors involvement can jointly affect academic performance. The factors are: Student’s interest, Relationship status, Entrepreneurial activities, Peer influence, Health and family background. The traditional decision support system architecture was extended in this study by introducing two components: Fuzzy engine and Mining Engine. Fuzzy engine was introduced to capture intra uncertainties in students' judgment about the data gathered and Mining engine to extract hidden and previously unknown interesting patterns from the dataset. The predictive model was established using fuzzy association rule mining technique. The dataset was gathered using one-on-one questionnaire interaction with students from 4 Universities in Nigeria. The system evaluates students' linguistic levels of involvement and predicts the possible class of honours for them with explicit interpretation of the fired patterns. This system will assist the students in decision making as to the extent they can be involved in some socio-economic activities relative to their family and health status in order to have their desired classes of honour
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