130,868 research outputs found

    MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems

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    Many algorithms have emerged to address the discovery of quantitative association rules from datasets in the last years. However, this task is becoming a challenge because the processing power of most existing techniques is not enough to handle the large amount of data generated nowadays. These vast amounts of data are known as Big Data. A number of previous studies have been focused on mining boolean or nominal association rules from Big Data problems, nevertheless, the data in real-world applications usually consist of quantitative values and designing data mining algorithms able to extract quantitative association rules presents a challenge to workers in this research field. In spite of the fact that we can find classical methods to discover boolean or nominal association rules in the most well-known repositories of Big Data algorithms, such repositories do not provide methods to discover quantitative association rules. Indeed, no methodologies have been proposed in the literature without prior discretization in Big Data. Hence, this work proposes MRQAR, a new generic parallel framework to discover quantitative association rules in large amounts of data, designed following the MapReduce paradigm using Apache Spark. MRQAR performs an incremental learning able to run any sequential quantitative association rule algorithm in Big Data problems without needing to redesign such algorithms. As a case study, we have integrated the multiobjective evolutionary algorithm MOPNAR into MRQAR to validate the generic MapReduce framework proposed in this work. The results obtained in the experimental study performed on five Big Data problems prove the capability of MRQAR to obtain reduced set of high quality rules in reasonable time.Ministerio de Economía y Competitividad TIN2017-89517-PMinisterio de Economía y Competitividad TIN2014-55894-C2-1-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-

    Reducing gaps in quantitative association rules: A genetic programming free-parameter algorithm

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    The extraction of useful information for decision making is a challenge in many different domains. Association rule mining is one of the most important techniques in this field, discovering relationships of interest among patterns. Despite the mining of association rules being an area of great interest for many researchers, the search for well-grouped continuous values is still a challenge, discovering rules that do not comprise patterns which represent unnecessary ranges of values. Existing algorithms for mining association rules in continuous domains are mainly based on a non-deterministic search, requiring a high number of parameters to be optimised. These parameters hinder the mining process, and the algorithms themselves must be known to those data mining experts that want to use them. We therefore present a grammar guided genetic programming algorithm that does not require as many parameters as other existing approaches and enables the discovery of quantitative association rules comprising small-size gaps. The algorithm is verified over a varied set of data, comparing the results to other association rule mining algorithms from several paradigms. Additionally, some resulting rules from different paradigms are analysed, demonstrating the effectiveness of our model for reducing gaps in numerical features

    Re-mining positive and negative association mining results

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    Positive and negative association mining are well-known and extensively studied data mining techniques to analyze market basket data. Efficient algorithms exist to find both types of association, separately or simultaneously. Association mining is performed by operating on the transaction data. Despite being an integral part of the transaction data, the pricing and time information has not been incorporated into market basket analysis so far, and additional attributes have been handled using quantitative association mining. In this paper, a new approach is proposed to incorporate price, time and domain related attributes into data mining by re-mining the association mining results. The underlying factors behind positive and negative relationships, as indicated by the association rules, are characterized and described through the second data mining stage re-mining. The applicability of the methodology is demonstrated by analyzing data coming from apparel retailing industry, where price markdown is an essential tool for promoting sales and generating increased revenue

    Quantitative temporal association rule mining by genetic algorithm

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    Association rule mining has shown great potential to extract knowledge from multidimensional data sets. However, existing methods in the literature are not effectively applicable to quantitative temporal data. This article extends the concepts of association rule mining from the literature. Based on the extended concepts is presented a method to mine rules from multidimensional temporal quantitative data sets using genetic algorithm, called GTARGA, in reference to Quantitative Temporal Association Rule Mining by Genetic Algorithm. Experiments with QTARGA in four real data sets show that it allows to mine several high-confidence rules in a single execution of the method

    A Mining Algorithm under Fuzzy Taxonomic Structures

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    Most conventional data-mining algorithms identify the relationships among transactions using binary values and find rules at a single concept level. Transactions with quantitative values and items with taxonomic relations are, however, commonly seen in real-world applications. Besides, the taxonomic structures may also be represented in a fuzzy way. This paper thus proposes a fuzzy multiple-level mining algorithm for extracting fuzzy association rules under given fuzzy taxonomic structures. The proposed algorithm adopts a top-down progressively deepening approach to finding large itemsets. It integrates fuzzy-set concepts, data-mining technologies and multiple-level fuzzy taxonomy to find fuzzy association rules from given transaction data sets. Each item uses only the linguistic term with the maximum cardinality in later mining processes, thus making the number of fuzzy regions to be processed the same as the number of the original items. The algorithm therefore focuses on the most important linguistic terms for reduced time complexit

    A Fuzzy Mining Algorithm for Association-Rule Knowledge Discovery

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    Due to increasing use of very large database and data warehouses, discovering useful knowledge from transactions is becoming an important research area. On the other hand, using fuzzy classification in data mining has been developed in recent years. Hong and Lee proposed a general learning method that automatically derives fuzzy if-then rules and membership functions from a set of given training examples using a decision table. But it is complex if there are many attributes or if the predefined unit is small. Hong and Chen improve it by first selecting relevant attributes and building appropriate initial membership functions. Based on Hong’s heuristic algorithm of membership functions and Apriori approach, we propose a fuzzy mining algorithm to explore association rules from given quantitative transactions. Experimental results on Iris data show that the proposed algorithm effectively induces more association rules

    Mining Fuzzy Coherent Rules from Quantitative Transactions Without Minimum Support Threshold

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    [[abstract]]Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, some comment problems of those approaches are that (1) a minimum support should be predefined, and it is hard to set the appropriate one, and (2) the derived rules usually expose common-sense knowledge which may not be interested in business point of view. In this paper, we thus proposed an algorithm for mining fuzzy coherent rules to overcome those problems with the properties of propositional logic. It first transforms quantitative transactions into fuzzy sets. Then, those generated fuzzy sets are collected to generate candidate fuzzy coherent rules. Finally, contingency tables are calculated and used for checking those candidate fuzzy coherent rules satisfy four criteria or not. Experiments on the foodmart dataset are also made to show the effectiveness of the proposed algorithm.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20120610~20120615[[iscallforpapers]]Y[[conferencelocation]]Brisbane, Australi

    Mining Quantitative Association Rules in Microarray Data Using Evolutive Algorithms

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    The microarray technique is able to monitor the change in concentration of RNA in thousands of genes simultaneously. The interest in this technique has grown exponentially in recent years and the difficulties in analyzing data from such experiments, which are characterized by the high number of genes to be analyzed in relation to the low number of experiments or samples available. In this paper we show the result of applying a data mining method based on quantitative association rules for microarray data. These rules work with intervals on the attributes, without discretizing the data before. The rules are generated by an evolutionary algorithm.Ministerio de Ciencia y Tecnología TIN2007-68084-C-00Junta de Andalucía P07-TIC-0261

    Mining Quantitative Association Rules (Quant Miner) menggunakan Algoritma Genetika

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    ABSTRAKSI: Saat ini, perkembangan ekonomi di sektor indrustri mengalami perkembangan yang sangat pesat. Terutama perusahaan atau organisasi yang telah mengumpulkan data sekian tahun lamanya seperti data pembelian, data penjualan,data nasabah,data transaksi dsb. Hampir semua data tersebut dimasukkan dengan menggunakan aplikasi komputer yang digunakan untuk menangani transaksi sehari-hari yang kebanyakan merupakan OLTP (On Line Transaction Processing). Oleh karena itu, agar data-data yang sudah terkumpul tersebut dapat digali untuk mendapatkan sesuatu yang berharga yang dapat dijadikan sebagai pengetahuan suatu perusahaan dalam hal pengambilan suatu keputusan. Dengan demikian, diperlukan suatu sistem data mining dengan teknik asosiasi untuk menunjukkan kondisi nilai atribut yang terjadi secara bersamaan di dalam sekumpulan data. Terutama penanganan data transaksional yang bertipe atribut kategorikal dan numerik.Hasil akhir dari teknik asosiasi adalah menemukan pengetahuan yang merepresentasikan aturan (rule) terhadap data yang diproses. Dalam Tugas Akhir ini digunakan suatu Algoritma Genetika untuk memproses data dengan teknik asosiasi. Data yang digunakan dalam Tugas Akhir ini adalah data pelanggan telkom sebagai data mentah dan data iris.Dalam Tugas Akhir ini, mengimplementasikan proses algoritma genetika untuk mencari rule asosiasi yang terbaik yang memenuhi nilai minimal support dan minimal confidence.Setelah dilakukan percobaan dengan algoritma genetika terbukti bahwa perangkat lunak mining quantitative association rules (quant miner) menggunakan algoritma genetika berhasil menghasilkan rules asosiasi terbaik. Kata Kunci : Kata kunci : OLTP (On Line Transaction Processing), data mining, teknik asosiasi, algoritma genetika, rules asosiasi, support, confidence, minimal support, minimal confidence, kategorikal, numerikABSTRACT: For this time, the growth of economics in industry expanding fastly.Especially, company or organization has been collected data for many years for example transaction data. Almost all of data entered using with computer application to use for handling daily trancaction commonly is OLTP (On Line Transaction Processing). Because of , all of data has been collected can use to get something cost to find knowledge a company to decide decision Because that, needs a system data mining use association technique to show value an attribute has happened together in data collected. Especially, handling of transactional data with categorical and numeric attribute. Final result from association technique is to find knowledge representation rules in processing data. In this final project, implemented genetic algorithm to find best association rules with minimal support and minimal confidence. After conducted with the attempt, mining quantitative association rules using genetics algorithm proven to found best association rulesKeyword: Keywords : OLTP (On Line Transaction Processing), data mining, association technique, genetics algorithm, association rules, support, confidence, minimal support, minimal confidence, categorical, numeric
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