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What is Distributed Association Rule Mining (DARM)

Chapter 4
Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence
Distributed association rule mining technique for a vertical partitioned data set across several sites. Let I = {i1, i2, .in} be a set of items and T = {T1, T2… Tn} be a set of transactions where each T? I, i. A transaction Ti contains an item set X?I only if I, X ?T. An association rule associated is of the form X ?Y(X ?Y ? 0) with support S and confidence C if S% of the transactions in T contains X?Y and C% of transactions that contain X also contain Y. In a horizontally partitioned Data base, the transactions are distributed among n sites. Support (X ?Y) = probe (X?Y) /Total number of transaction the global support count of an item set is the union or product of all local support counts. Support g (X) = Support1(x) ?Support2(x) ?…?Support n(x). Confidence (X ?Y) = Support (X ?Y) / Support(X). The global confidence of a rule can be expressed in terms of the global support. Confidence g (X ?Y) = Support g (X ?Y) / Support g(X). The aim of the distributed association rule mining is to discover all rules with global support and global confidence greater than the user specified minimum support and confidence. The subsequent steps, utilizing the secure sum and secure set union methods described earlier are used. The basis of the algorithm is the Apriori algorithm, which use the (k-1) sized frequent item sets to generate the k sized frequent item sets.
Published in Chapter:
Secure Data Analysis in Clusters (Iris Database)
Raghvendra Kumar (LNCT College, India), Prasant Kumar Pattnaik (KIIT University (Deemed), India), and Priyanka Pandey (LNCT College, India)
Copyright: © 2017 |Pages: 10
DOI: 10.4018/978-1-5225-2031-3.ch004
Abstract
This chapter used privacy preservation techniques (Data Modification) to ensure Privacy. Privacy preservation is another important issue. A picture, where number of clients owning their clustered databases (Iris Database) wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information and requires the privacy of the privileged information. There are numbers of efficient protocols are required for privacy preserving in data mining. This chapter presented various privacy preserving protocols that are used for security in clustered databases. The Xln(X) protocol and the secure sum protocol are used in mutual computing, which can defend privacy efficiently. Its focuses on the data modification techniques, where it has been modified our distributed database and after that sanded that modified data set to the client admin for secure data communication with zero percentage of data leakage and also reduce the communication and computation complexity.
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