IPOC: an efficient approach for dynamic association rule generation using incremental data with updating supports

According to recent statistics, there was drastic growth in online business sector where more number of customers intends to purchase items. Due to these retailers accumulates huge volumes of data from day to day operations and engrossed in analyzing the data to watch the behavior of customers at items which strengthen the business promotions and catalog management. It reveals the customer interestingness and frequent items from large data. To carry out this there was known algorithms present which deals with static and dynamic data. Some of them are lag time and memory consuming and involves unnecessary process. This paper intents to implement an efficient incremental pre ordered coded tree (IPOC) generation for data updates and applies frequent item set generation algorithm on the tree. While incremental generation of tree, new data items will link to previous nodes in tree by increasing its support count. This removes the lagging issues in existing algorithms and does not need to mine from scratch and also reduces the time, memory consumption by the use of nodeset data structure. The results of proposed method was observed and analyzed with existing methods. The anticipated method shows improved results by means of generated items, time and memory.

Using Rough Set Theory to Find Minimal Log with Rule Generation

Data pre-processing is a major difficulty in the knowledge discovery process, especially feature selection on a large amount of data. In literature, various approaches have been suggested to overcome this difficulty. Unlike most approaches, Rough Set Theory (RST) can discover data de-pendency and reduce the attributes without the need for further information. In RST, the discernibility matrix is the mathematical foundation for computing such reducts. Although it proved its efficiency in feature selection, unfortunately it is computationally expensive on high dimensional data. Algorithm complexity is related to the search of the minimal subset of attributes, which requires computing an exponential number of possible subsets. To overcome this limitation, many RST enhancements have been proposed. Contrary to recent methods, this paper implements RST concepts in an iterated manner using R language. First, the dataset was partitioned into a smaller number of subsets and each subset processed independently to generate its own minimal attribute set. Within the iterations, only minimal elements in the discernibility matrix were considered. Finally, the iterated outputs were compared, and those common among all reducts formed the minimal one (Core attributes). A comparison with another novel proposed algorithm using three benchmark datasets was performed. The proposed approach showed its efficiency in calculating the same minimal attribute sets with less execution time.

A Biological Data-Driven Mining Technique by Using Hybrid Classifiers With Rough Set

Biological data classification and analysis are significant for living organs. A biological data classification is an approach that classifies the organs into a particular group based on their features and characteristics. The objective of this paper is to establish a hybrid approach with naive Bayes, apriori algorithm, and KNN classifier that generates optimal classification rules for finding biological pattern matching. The authors create combined association rules by using naïve Bayes and apriori approach with a rough set for next sequence prediction. First, the large DNA sequence is reduced by using k-nearest approach. They apply association rules by using naïve Bayes and apriori approach for the next sequence pattern. The hybrid approach provides more accuracy than single classifier for biological sequence prediction. The optimized hybrid process needs less execution time for rule generation for massive biological data analysis. The results established that the hybrid approach generally outperforms the other association rule generation approach.