Discovering association rules is one of the important tasks in data mining. While most of the existing algorithms are developed for efficient mining of frequent patterns, it has been noted recently that some of the infrequent patterns, such as negative associations and indirect associations, provide useful insight into the data. Existing indirect association mining algorithms mine indirect asso...

Mining association rules is an important data mining problem. Association rules are usually mined repeatedly in different parts of a database. Current algorithms for mining association rules work in two steps. First, the most frequently occurring sets of items are discovered, then the sets are used to generate the association rules. The first step usually requires repeated passes over the analy...

Frequent item sets mining plays an important role in association rules mining. Over the years, a variety of algorithms for finding frequent item sets in very large transaction databases have been developed. The main focus of this paper is to analyze the implementations of the Frequent item set Mining algorithms such as SMine and Apriori Algorithms. General Terms-Data Mining, Frequent Item sets,...

In this paper we develop data mining techniques to predict 3D contact potentials among protein residues (or amino acids) based on the hierarchical nucleationpropagation model of protein folding. We apply a hybrid approach, using a Hidden Markov Model to extract folding initiation sites, and then apply association mining to discover contact potentials. The new hybrid approach achieves accuracy r...

In this paper we study two orthogonal extensions of the classical data mining problem of mining association rules, and show how they naturally interact. The first is the extension from a propositional representation to datalog, and the second is the condensed representation of frequent itemsets by means of Formal Concept Analysis (FCA). We combine the notion of frequent datalog queries with ice...

Association rules are essential data mining tool and as such has been well researched. Many new types of association rules based on both categorial or quantitative data have been founded ([8], [7], [2], [4]). Our work is directed to the theoretical features of association rules; especially, we study a specific class of association rules called δ-cosymmetric rules. We present here some interesti...

Recently, high utility pattern or itemset mining has become the most important research issues in data mining. In high utility itemset mining, the profit values for every item are considered. Generating high utility itemsets from a set of transactions in horizontal data format is a common practice. We hereby present the study of issues related to the different structures used and algorithms for...

We present Viper, for Visual Pattern Explorer, an innovative, browser-based application for interactive pattern exploration, assisted by visualisation, recommendation, and algorithmic search. The target audience consists of domain experts who have access to data but not to –potentially expensive– data mining experts. The goal of the system is to enable the target audience to perform true explor...

R.C. Jain Department of Computer Applications, Samrat Ashok Technological Institute, Vidisha, M.P. E-mail: [email protected] -------------------------------------------------------------------ABSTRACT----------------------------------------------------------------Association rule mining and classification are two important techniques of data mining in knowledge discovery process. Integration...

We describe a frequent itemset mining algorithm and implementation based on the well-known algorithm FPgrowth. The theoretical difference is the main data structure (tree), which is more compact and which we do not need to rebuild for each conditional step. We thoroughly deal with implementation issues, data structures, memory layout, I/O and library functions we use to achieve comparable perfo...