1. A Study on Efficient Data Mining Approach on Compressed Transaction (2011)
Data mining can be viewed as a result of the natural evolution of information technology. The spread of computing has led to an explosion in the volume of data to be stored on hard disks and sent over the Internet. This growth has led to a need for data compression, that is, the ability to reduce the amount of storage or Internet bandwidth required to handle the data. This paper analysis the various data mining approaches which is used to compress the original database into a smaller one and perform the data mining process for compressed transaction such as M2TQT,PINCER-SEARCH algorithm, APRIORI & ID3 algorithm, TM algorithm, AIS & SETM, CT-Apriori algorithm, CBMine, CT-ITL algorithm, FIUT-Tree. Among the various techniques M2TQT uses the relationship of transactions to merge related transactions and builds a quantification table to prune the candidate item sets which are impossible to become frequent in order to improve the performance of mining association rules. Thus M2TQT is observed to perform better than existing approaches.
2. An Efficient Algorithm for Mining Multilevel Association Rule Based on Pincer Search (2012)
Discovering frequent itemset is a key difficulty in significant data mining applications, such as the discovery of association rules, strong rules, episodes, and minimal keys. The problem of developing models and algorithms for multilevel association mining poses for new challenges for mathematics and computer science. In this paper, we present a model of mining multilevel association rules which satisfies the different minimum support at each level, we have employed princer search concepts, multilevel taxonomy and different minimum supports to find multilevel association rules in a given transaction data set. This search is used only for maintaining and updating a new data structure. It is used to prune early candidates that would normally encounter in the top-down search. A main characteristic of the algorithms is that it does not require explicit examination of every frequent itemsets, an example is also given to demonstrate and support that the proposed mining algorithm can derive the multiple-level association rules under different supports in a simple and effective manner
3. Fast Mining of Fuzzy Association Rules (2012)
Fuzzy association rules described by the natural language are well suited for the thinking of human subject and will help to increase the flexibility for supporting user in making decisions or designing the fuzzy systems. However, the efficiency of algorithms needs to be improved to handle real-world large datasets. In this paper, we present an efficient algorithm named fuzzy cluster-based (FCB) along with its parallel version named parallel fuzzy cluster-based (PFCB). The FCB method is to create cluster tables by scanning the database once, and then clustering the transaction records to the i-th cluster table, where the length of a record is i. moreover, the fuzzy large itemsets are generated by contrasts with the partial cluster tables. Similarly, the PFCB method is to create cluster tables by scanning the database once, and then clustering the transaction records to the i-th cluster table, which is on the i-th processor, where the length of a record is i. moreover, the large itemsets are generated by contrasts with the partial cluster tables. Then, to calculate the fuzzy support of the candidate itemsets at each level, each processor calculates the support of the candidate itemsets in its own cluster and forwards the result to the coordinator. The final fuzzy support of the candidate itemsets is then calculated from these results in the coordinator. We have performed extensive experiments and compared the performance of our algorithms with two of the best existing algorithms.
4. Detection of Fuzzy Association Rules by Fuzzy Transforms (2012)
Ferdinando DiMartino and Salvatore Sessa
Dipartimento di Costruzioni e Metodi Matematici in Architettura, Universit`a degli Studi di Napoli Federico II, Via Monteoliveto 3, 80134 Napoli, Italy Correspondence should be addressed to Salvatore Sessa, sessa@unina.it Received 3 March 2012; Revised 25 June 2012; Accepted 25 June 2012 Academic Editor: Irina G. Perfilieva Copyright © 2012 F. Di Martino and S. Sessa. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We present a new method based on the use of fuzzy transforms for detecting coarse-grained association rules in the datasets. The fuzzy association rules are represented in the form of linguistic expressions and we introduce a pre-processing phase to determine the optimal fuzzy partition of the domains of the quantitative attributes. In the extraction of the fuzzy association rules we use the AprioriGen algorithm and a confidence index calculated via the inverse fuzzy transform. Our method is applied to datasets of the 2001 census database of the district of Naples (Italy); the results show that the extracted fuzzy association rules provide a correct coarse-grained view of the data association rule set.
5. Fuzzy Associative Rule-based Approach for Pattern Mining and Identification and Pattern-based Classification (2011)
Associative Classification leverages Association Rule Mining (ARM) to train Rule-based classifiers. The classifiers are built on high quality Association Rules mined from the given dataset. Associative Classifiers are very accurate because Association Rules encapsulate all the dominant and statistically significant relationships between items in the dataset. They are also very robust as noise in the form of insignificant and low-frequency itemsets are eliminated during the mining and training stages. Moreover, the rules are easy-to-comprehend, thus making the classifier transparent. Conventional Associative Classification and Association Rule Mining (ARM) algorithms are inherently designed to work only with binary attributes, and expect any quantitative attributes to be converted to binary ones using ranges, like “Age = [25, 60]”. In order to mitigate this constraint, Fuzzy logic is used to convert quantitative attributes to fuzzy binary attributes, like “Age = Middle-aged”, so as to eliminate any loss of information arising due to sharp partitioning, especially at partition boundaries, and then generate Fuzzy Association Rules using an appropriate Fuzzy ARM algorithm. These Fuzzy Association Rules can then be used to train a Fuzzy Associative Classifier. In this paper, we also show how Fuzzy Associative Classifiers so built can be used in a wide variety of domains and datasets, like transactional datasets and image datasets.
6. Efficient Parallel Pruning of Associative Rules with Optimized Search (2012)
The main focus of this research work is to propose an improved association rule mining algorithm to minimize the number of candidate sets while generating association rules with efficient pruning time and search space optimization. The relative association with reduced candidate item set reduces the overall execution time. The scalability of this work is measured with number of item sets used in the transaction and size of the data set. Further Fuzzy based rule mining principle is adapted in this work to obtain more informative associative rules and frequent items with increased sensitive. The requirement for sensitive items is to have a semantic connection between the components of the item-value pairs. The effectiveness of item-value pairs minimizes the search space to its optimality. Optimality of the search space indicates the trade off between pruning time and size of the data set.
7. Using Support Vector Machine in Fuzzy Association Rule Mining (2012)
Fuzzy rule based classification systems is one of the most popular in pattern classification problems. The rules in the fuzzy models can be weighted to show the importance of generated rules where all attributes in the antecedent part of the rules have been usually weighted equally. Whereas the contributed attributes in a fuzzy model may have different influences on the decision making, a new method based on support vector machine-recursive feature elimination (SVM-RFE) has been proposed in this study to show the effects of attributes by weighting factors. Apriori algorithm and fuzzy association rule mining (FARM) have been used to generate the suitable rules which are weighted by fuzzy support value. The combination of the proposed method for attribute weighting and fuzzy support value for weighting the generated rules have been used to discriminate the samples of two different well known datasets iris and wine. The results show that this simple method can increase the rate of accuracy and reduce the dependency of model to fuzzy support value in Apriori algorithm and the number of rules.
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