Analysis of Measures of Quantitative Association Rules

This paper presents the analysis of relationships among different interestingness measures of quality of association rules as first step to select the best objectives in order to develop a multi-objective algorithm. For this purpose, the discovering of association rules is based on evolutionary techniques. Specifically, a genetic algorithm has been used in order to mine quantitative association rules and determine the intervals on the attributes without discretizing the data before. The algorithm has been applied in real-word climatological datasets based on Ozone and Earthquake data.Ministerio de Ciencia y Tecnología TIN2007-68084-C-00Junta de Andalucía P07-TIC-0261

Mining Quantitative Association Rules (Quant Miner) menggunakan Algoritma Genetika

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

Inferring Gene-Gene Associations from Quantitative Association Rules

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. Microarray experiments are generating datasets that can help in reconstructing gene networks. One of the most important problems in network reconstruction is finding, for each gene in the network, which genes can affect it and how. Association Rules are an approach of unsupervised learning to relate attributes to each other. In this work we use Quantitative Association Rules in order to define interrelations between genes. These rules work with intervals on the attributes, without discretizing the data before and they are generated by a multi-objective evolutionary algorithm. In most cases the extracted rules confirm the existing knowledge about cell-cycle gene expression, while hitherto unknown relationships can be treated as new hypotheses.Ministerio de Ciencia y Tecnología TIN2007-68084-C-00Junta de Andalucía P07-TIC-0261

Obtaining optimal quality measures for quantitative association rules

There exist several works in the literature in which fitness functions based on a combination of weighted measures for the discovery of association rules have been proposed. Nevertheless, some differences in the measures used to assess the quality of association rules could be obtained according to the values of the weights of the measures included in the fitness function. Therefore, user's decision is very important in order to specify the weights of the measures involved in the optimization process. This paper presents a study of well-known quality measures with regard to the weights of the measures that appear in a fitness function. In particular, the fitness function of an existing evolutionary algorithm called QARGA has been considered with the purpose of suggesting the values that should be assigned to the weights, depending on the set of measures to be optimized. As initial step, several experiments have been carried out from 35 public datasets in order to show how the weights for confidence, support, amplitude and number of attributes measures included in the fitness function have an influence on different quality measures according to several minimum support thresholds. Second, statistical tests have been conducted for evaluating when the differences in measures of the rules obtained by QARGA are significative, and thus, to provide the best weights to be considered depending on the group of measures to be optimized. Finally, the results obtained when using the recommended weights for two real-world applications related to ozone and earthquakes are reported.Ministerio de Ciencia y Tecnología TIN2011-28956-C02Junta de Andalucía P12- TIC-1728Universidad Pablo de Olavide APPB81309

A fuzzy approach for mining quantitative association rules

During the last ten years, data mining, also known as knowledge discovery in databases, has established its position as a prominent and important research area. Mining association rules is one of the important research problems in data mining. Many algorithms have been proposed to find association rules in databases with quantitative attributes. The algorithms usually discretize the attribute domains into sharp intervals, and then apply simpler algorithms developed for boolean attributes. An example of a quantitative association rule might be "10% of married people between age 50 and 70 have at least 2 cars". Recently, fuzzy sets were suggested to represent intervals with non-sharp boundaries. Using the fuzzy concept, the above example could be rephrased e.g. "10% of married old people have several cars". However, if the fuzzy sets are not well chosen, anomalies may occur. In this paper we tackle this problem by introducing an additional fuzzy normalization process. Then we present the definition of quantitative association rules based on fuzzy set theory and propose a new algorithm for mining fuzzy association rules. The algorithm uses generalized definitions for interest measures. Experimental results show the efficiency of the algorithm for large databases

Quantitative Association Rules Applied to Climatological Time Series Forecasting

This work presents the discovering of association rules based on evolutionary techniques in order to obtain relationships among correlated time series. For this purpose, a genetic algorithm has been proposed to determine the intervals that form the rules without discretizing the attributes and allowing the overlapping of the regions covered by the rules. In addition, the algorithm has been tested on real-world climatological time series such as temperature, wind and ozone and results are reported and compared to that of the well-known Apriori algorithm

Selecting the best measures to discover quantitative association rules

The majority of the existing techniques to mine association rules typically use the support and the confidence to evaluate the quality of the rules obtained. However, these two measures may not be sufficient to properly assess their quality due to some inherent drawbacks they present. A review of the literature reveals that there exist many measures to evaluate the quality of the rules, but that the simultaneous optimization of all measures is complex and might lead to poor results. In this work, a principal components analysis is applied to a set of measures that evaluate quantitative association rules' quality. From this analysis, a reduced subset of measures has been selected to be included in the fitness function in order to obtain better values for the whole set of quality measures, and not only for those included in the fitness function. This is a general-purpose methodology and can, therefore, be applied to the fitness function of any algorithm. To validate if better results are obtained when using the function fitness composed of the subset of measures proposed here, the existing QARGA algorithm has been applied to a wide variety of datasets. Finally, a comparative analysis of the results obtained by means of the application of QARGA with the original fitness function is provided, showing a remarkable improvement when the new one is used.Ministerio de Ciencia y Tecnología TIN2011-28956-C0

A Sensitivity Analysis for Quality Measures of Quantitative Association Rules

There exist several fitness function proposals based on a combination of weighted objectives to optimize the discovery of association rules. Nevertheless, some differences in the measures used to assess the quality of association rules could be obtained according to the values of such weights. Therefore, in such proposals it is very important the user’s decision in order to specify the weights or coefficients of the optimized objectives. Thus, this work presents an analysis on the sensitivity of several quality measures when the weights included in the fitness function of the existing QARGA algorithm are modified. Finally, a comparative analysis of the results obtained according to the weights setup is provided.MICYT TIN2011-28956-C02-00Junta de Andalucía P11-TIC-752

Mining Quantitative Association Rules in Microarray Data Using Evolutive Algorithms

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