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Adhikari A.,Parvatibai Chowgule College | Adhikari J.,Narayan Zantye College
Intelligent Systems Reference Library | Year: 2015

A large class of problems is concerned with temporal data. Identifying temporal patterns in these datasets is a fully justifiable as well as an important task. Recently, researchers have reported an algorithm for finding calendar-based periodic pattern in a time-stamped data and introduced the concept of certainty factor in association with an overlapped interval. In this chapter, we have extended the concept of certainty factor by incorporating support information for effective analysis of overlapping intervals. We have proposed a number of improvements of the algorithm for identifying calendar-based periodic patterns. In this direction we have proposed a hash based data structure for storing and managing patterns. Based on this modified algorithm, we identify full as well as partial periodic calendar-based patterns. We provide a detailed data analysis incorporating various parameters of the algorithm and make a comparative analysis with the existing algorithm, and show the effectiveness of our algorithm. Experimental results are provided on both real and synthetic databases. © Springer International Publishing Switzerland 2015. Source


Adhikari A.,Parvatibai Chowgule College | Adhikari J.,Narayan Zantye College
Intelligent Systems Reference Library | Year: 2015

Many multi-branch organizations transact from different branches, and the transactions are stored locally. The number of multi-branch companies as well as the number of branches of a multi-branch company is increasing over time. Thus, it is important to study data mining on related data sources. A global exceptional pattern describes interesting individuality of few branches. Therefore, it is interesting to identify such patterns. The gist of the chapter is given as follows: (i) Type I and type II global exceptional frequent itemsets in multiple data sources are presented. (ii) The notion of exceptional sources for a type II global exceptional frequent itemset is discussed. (iii) Also the type I and type II global exceptional association rules in multiple data sources are discussed. (iv) An algorithm for synthesizing type II global exceptional frequent itemsets is designed. Experimental results are presented on both artificial and real datasets. We also compare this algorithm with the existing algorithm theoretically and experimentally. The experimental results show that the proposed algorithm is effective. © Springer International Publishing Switzerland 2015. Source


Adhikari A.,Parvatibai Chowgule College | Adhikari J.,Narayan Zantye College
Intelligent Systems Reference Library | Year: 2015

With the advancement of technologies, mass storage devices are now capable of storing more data. Also, they have become cheaper. Moreover varieties of data collection channels are now available in the market. Data mining is an emerging field of study, and has been applied to various domains. Some new patterns such as conditional pattern, arbitrary Boolean expression induced by itemset, type I global exceptional itemset and type II global exceptional itemset are discussed in this book. Also, some association measures viz., A1, A2, association rules induced by item and quantity, overall association between items, heavy association rule, exceptional association rule, simi1 and simi2 and influence of an item on another item, are reported in different chapters. © Springer International Publishing Switzerland 2015. Source


Adhikari A.,Parvatibai Chowgule College | Adhikari J.,Narayan Zantye College
Intelligent Systems Reference Library | Year: 2015

The model of local pattern analysis provides sound solutions to many multi-database mining problems. In this chapter, we discuss different types of extreme association rules in multiple databases viz., heavy association rule, high-frequency association rule, low-frequency association rule, and exceptional association rule. Also, we show how one can apply the model of local pattern analysis systematically and effectively. For this purpose, an extended model of local pattern analysis is presented. The extended model has been applied to mine heavy association rules in multiple databases. Also, we justify why the extended model works more effectively. An algorithm for synthesizing heavy association rule in multiple databases is given. Furthermore, we show that the algorithm identifies whether a heavy association rule is high-frequency rule or exceptional rule. Experimental results are provided for both synthetic and real-world datasets and a detailed error analysis is carried out. Furthermore, we present a comparative analysis by contrasting the proposed algorithm with some of those reported in the literature. This analysis is completed by taking into consideration the criteria of execution time and average error. © Springer International Publishing Switzerland 2015. Source


Adhikari A.,Parvatibai Chowgule College | Adhikari J.,Narayan Zantye College
Intelligent Systems Reference Library | Year: 2015

In view of answering queries provided in multiple large databases, it might be required to mine relevant databases en block. In this chapter, we present an effective solution to clustering multiple large databases. Two measures of similarity between a pair of databases are presented and study their main properties. In the sequel, we design an algorithm for clustering multiple databases based on an introduced similarity measure. Also, we present a coding, referred to as IS coding, to represent itemsets space efficiently. The coding of this nature enables more frequent itemsets to participate in the determination of the similarity between two databases. Thus the invoked clustering process becomes more accurate. We also show that the IS coding attains maximum efficiency in most of the cases of the mining processes. The clustering algorithm becomes improved (in terms of its time complexity) when contrasted with the existing clustering algorithms. The efficiency of the clustering process has been improved using several strategies that is by reducing execution time of the clustering algorithm, using more suitable similarity measure, and storing frequent itemsets space efficiently. © Springer International Publishing Switzerland 2015. Source

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