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Adhikari A.,P.A. College | Ramachandrarao P.,Goa University | Pras B.,Florida A&M University | Adhikari J.,Narayan Zantye College
International Arab Journal of Information Technology | Year: 2010

Effective data analysis using multiple databases requires highly accurate patterns. Local pattern analysis might extract low quality patterns from multiple large databases. Thus, it is necessary to improve mining multiple databases using local pattern analysis. We present existing specialized as well as generalized techniques for mining multiple large databases. We formalize the idea of multi-database mining using local pattern analysis and propose a new generalized technique for mining multiple large databases. It improves the quality of synthesized global patterns significantly. We conduct experiments on both real and synthetic databases to judge the effectiveness of the proposed technique.


Adhikari J.,Narayan Zantye College | Rao P.R.,Goa University
Pattern Recognition Letters | Year: 2010

Influence of items on some other items might not be the same as the association between these sets of items. Many tasks of data analysis are based on expressing influence of items on other items. In this paper, we introduce the notion of an overall influence of a set of items on another set of items. We also propose an extension to the notion of overall association between two items in a database. Using the notion of overall influence, we have designed two algorithms for influence analysis involving specific items in a database. As the number of databases increases on a yearly basis, we have adopted incremental approach in these algorithms. Experimental results are reported for both synthetic and real-world databases. © 2009 Elsevier B.V. All rights reserved.


Adhikari J.,Narayan Zantye College
Journal of Intelligent Systems | Year: 2014

A large class of problems deals with temporal data. Identifying temporal patterns in these datasets is a natural as well as an important task. In recent times, researchers have reported an algorithm for finding calendar-based periodic pattern in time-stamped data without considering the purchased quantities of the items. However, most of the real-life databases are nonbinary, and therefore, exploring various calendar-based patterns (yearly, monthly, weekly, daily) with their purchased quantities may discover information useful to improve the quality of business decisions. In this article, a technique is proposed to extract calendar-based periodic patterns from nonbinary transactions. In this connection, the concept of certainty factor has been introduced by incorporating transaction frequency for overlapped intervals. Algorithms have been designed to mine frequent itemsets along with intervals and quantity. In addition to that, we have designed an algorithm to find the periodicity of the pattern. The algorithm is tested with real-life data, and the results are given. © 2014 by Walter de Gruyter Berlin/Boston 2014.


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.


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.


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.


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

Frequent itemsets determine major characteristics of a transactional database. An arbitrary Boolean expression can be thought as a generalized form of a query. It offers important knowledge to an organization. It is important to mine arbitrary Boolean expressions induced by frequent itemsets. In this chapter, we have introduced the concept of generator of an itemset, and showed that every Boolean function can be synthesized by its generator. The concept of conditional pattern has been introduced in Chap. 2. We discussed a simple and elegant framework for synthesizing generator of an itemset and designed an algorithm for this purpose. Experimental results are provided on four different databases © Springer International Publishing Switzerland 2015.


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

Many multi-branch companies transact from different branches. Each branch of the company maintains a separate database over time. The variation of sales of an item over time is an important issue, and therefore, we present the notion of stability of an item. Stable items are useful in making numerous strategic decisions of the company. We have discussed two measures of stability of an item. Based on the degree of stability of an item, an algorithm is designed for finding partition among items in different data sources. Then the notion of the best cluster is introduced by considering average degree of variation of a class, and designed an algorithm to find clusters among items in different data sources. The best cluster is determined by average degree of variation in a cluster. Experimental results are provided for three transactional databases. © Springer International Publishing Switzerland 2015.


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

Most of the real market basket data are non-binary in the sense that an item could be purchased multiple times in the same transaction. In this case, there are two types of occurrences of an itemset in a database: the number of transactions in the database containing the itemset, and the number of occurrences of the itemset in the database. Traditional support-confidence framework might not be adequate for extracting association rules in such a database. In this chapter, we introduce three categories of association rules. We introduce a framework based on traditional support-confidence framework for mining each category of association rules. We present experimental results based on two databases. ©.Springer International Publishing Switzerland 2015.


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.

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