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

Encapsulation of Soft Computing Approaches within Itemset Mining a A Survey

Data Mining discovers patterns and trends by extracting knowledge from large databases. Soft Computing techniques such as fuzzy logic, neural networks, genetic algorithms, rough sets, etc. aims to reveal the tolerance for imprecision and uncertainty for achieving tractability, robustness and low-cost solutions. Fuzzy Logic and Rough sets are suitable for handling different types of uncertainty. Neural networks provide good learning and generalization. Genetic algorithms provide efficient search algorithms for selecting a model, from mixed media data. Data mining refers to information extraction while soft computing is used for information processing. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Association rule mining (ARM) and Itemset mining focus on finding most frequent item sets and corresponding association rules, extracting rare itemsets including temporal and fuzzy concepts in discovered patterns. This survey paper explores the usage of soft computing approaches in itemset utility mining

Fuzzy association rules for biological data analysis: A case study on yeast

<p>Abstract</p> <p>Background</p> <p>Last years' mapping of diverse genomes has generated huge amounts of biological data which are currently dispersed through many databases. Integration of the information available in the various databases is required to unveil possible associations relating already known data. Biological data are often imprecise and noisy. Fuzzy set theory is specially suitable to model imprecise data while association rules are very appropriate to integrate heterogeneous data.</p> <p>Results</p> <p>In this work we propose a novel fuzzy methodology based on a fuzzy association rule mining method for biological knowledge extraction. We apply this methodology over a yeast genome dataset containing heterogeneous information regarding structural and functional genome features. A number of association rules have been found, many of them agreeing with previous research in the area. In addition, a comparison between crisp and fuzzy results proves the fuzzy associations to be more reliable than crisp ones.</p> <p>Conclusion</p> <p>An integrative approach as the one carried out in this work can unveil significant knowledge which is currently hidden and dispersed through the existing biological databases. It is shown that fuzzy association rules can model this knowledge in an intuitive way by using linguistic labels and few easy-understandable parameters.</p

Data Mining with Linguistic Thresholds

Abstract Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. In the past, the minimum supports and minimum confidences were set at numerical values. Linguistic minimum support and minimum confidence values are, however, more natural and understandable for human beings. This paper thus attempts to propose a new mining approach for extracting interesting weighted association rules from transactions, when the parameters needed in the mining process are given in linguistic terms. Items are also evaluated by managers as linguistic terms to reflect their importance, which are then transformed as fuzzy sets of weights. Fuzzy operations including fuzzy ranking are then used to find weighted large itemsets and association rules

Mining fuzzy association rules in large databases with quantitative attributes.

by Kuok, Chan Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 74-77).Abstract --- p.iAcknowledgments --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Data Mining --- p.2Chapter 1.2 --- Association Rule Mining --- p.3Chapter 2 --- Background --- p.6Chapter 2.1 --- Framework of Association Rule Mining --- p.6Chapter 2.1.1 --- Large Itemsets --- p.6Chapter 2.1.2 --- Association Rules --- p.8Chapter 2.2 --- Association Rule Algorithms For Binary Attributes --- p.11Chapter 2.2.1 --- AIS --- p.12Chapter 2.2.2 --- SETM --- p.13Chapter 2.2.3 --- "Apriori, AprioriTid and AprioriHybrid" --- p.15Chapter 2.2.4 --- PARTITION --- p.18Chapter 2.3 --- Association Rule Algorithms For Numeric Attributes --- p.20Chapter 2.3.1 --- Quantitative Association Rules --- p.20Chapter 2.3.2 --- Optimized Association Rules --- p.23Chapter 3 --- Problem Definition --- p.25Chapter 3.1 --- Handling Quantitative Attributes --- p.25Chapter 3.1.1 --- Discrete intervals --- p.26Chapter 3.1.2 --- Overlapped intervals --- p.27Chapter 3.1.3 --- Fuzzy sets --- p.28Chapter 3.2 --- Fuzzy association rule --- p.31Chapter 3.3 --- Significance factor --- p.32Chapter 3.4 --- Certainty factor --- p.36Chapter 3.4.1 --- Using significance --- p.37Chapter 3.4.2 --- Using correlation --- p.38Chapter 3.4.3 --- Significance vs. Correlation --- p.42Chapter 4 --- Steps For Mining Fuzzy Association Rules --- p.43Chapter 4.1 --- Candidate itemsets generation --- p.44Chapter 4.1.1 --- Candidate 1-Itemsets --- p.45Chapter 4.1.2 --- Candidate k-Itemsets (k > 1) --- p.47Chapter 4.2 --- Large itemsets generation --- p.48Chapter 4.3 --- Fuzzy association rules generation --- p.49Chapter 5 --- Experimental Results --- p.51Chapter 5.1 --- Experiment One --- p.51Chapter 5.2 --- Experiment Two --- p.53Chapter 5.3 --- Experiment Three --- p.54Chapter 5.4 --- Experiment Four --- p.56Chapter 5.5 --- Experiment Five --- p.58Chapter 5.5.1 --- Number of Itemsets --- p.58Chapter 5.5.2 --- Number of Rules --- p.60Chapter 5.6 --- Experiment Six --- p.61Chapter 5.6.1 --- Varying Significance Threshold --- p.62Chapter 5.6.2 --- Varying Membership Threshold --- p.62Chapter 5.6.3 --- Varying Confidence Threshold --- p.63Chapter 6 --- Discussions --- p.65Chapter 6.1 --- User guidance --- p.65Chapter 6.2 --- Rule understanding --- p.67Chapter 6.3 --- Number of rules --- p.68Chapter 7 --- Conclusions and Future Works --- p.70Bibliography --- p.7

Protecting big data mining association rules using fuzzy system

Recently, big data is granted to be the solution to opening the subsequent large fluctuations of increase in fertility. Along with the growth, it is facing some of the challenges. One of the significant problems is data security. While people use data mining methods to identify valuable information following massive database, people further hold the necessary to maintain any knowledge so while not to be worked out, like delicate common itemsets, practices, taxonomy tree and the like Association rule mining can make a possible warning approaching the secrecy of information. So, association rule hiding methods are applied to evade the hazard of delicate information misuse. Various kinds of investigation already prepared on association rule protecting. However, maximum of them concentrate on introducing methods with a limited view outcome for inactive databases (with only existing information), while presently the researchers facing the problem with continuous information. Moreover, in the era of big data, this is essential to optimize current systems to be suited concerning the big data. This paper proposes the framework is achieving the data anonymization by using fuzzy logic by supporting big data mining. The fuzzy logic grouping the sensitivity of the association rules with a suitable association level. Moreover, parallelization methods which are inserted in the present framework will support fast data mining process

Mining Target-Oriented Fuzzy Correlation Rules to Optimize Telecom Service Management

To optimize telecom service management, it is necessary that information about telecom services is highly related to the most popular telecom service. To this end, we propose an algorithm for mining target-oriented fuzzy correlation rules. In this paper, we show that by using the fuzzy statistics analysis and the data mining technology, the target-oriented fuzzy correlation rules can be obtained from a given database. We conduct an experiment by using a sample database from a telecom service provider in Taiwan. Our work can be used to assist the telecom service provider in providing the appropriate services to the customers for better customer relationship management.Comment: 10 pages, 7 table

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data