Discovering association rules from time-series data is an important data mining problem. The number of potential rules grows quickly as the number of items in the antecedent grows. It is therefore difficult for an expert to analyze the rules and identify the useful. An approach for generating representative association rules for transactions that uses only a subset of the set of frequent itemse...

Association rules have been widely used in gene expression data analysis. However, there is no systematical way to select interesting rules from the millions of rules generated from high dimensional gene expression data. In this study, a kernel density estimation based measurement is proposed to evaluate the interestingness of the association rules. Several pruning strategies are also devised t...

Concept lattice, core structure in Formal Concept Analysis has been used in various fields like software engineering and knowledge discovery.In this paper, we present the integration of Association rules and Classification rules using Concept Lattice. This gives more accurate classifiers for Classification. The algorithm used is incremental in nature. Any increase in the number of classes, attr...

Human health is affected by many environmental factors. Geographical detector is software based on spatial variation analysis of the geographical strata of variables to assess the environmental risks to human health: the risk detector indicates where the risk areas are; the factor detector identifies which factors are responsible for the risk; the ecological detector discloses the relative impo...

Document Summarization is a technique, which reduces the size of the documents and gives the outline and crisp information about the given group of documents. This paper introduces a new update summarization algorithm incorporating association rule mining and correlated concept based hierarchical clustering for dynamic environment. In this algorithm, the associated concepts are extracted using ...

Although association rule mining is an important pattern recognition and data analysis technique, extracting and finding significant rules from a large collection has always been challenging. The ability of information visualization to enable users to gain an understanding of high dimensional and large-scale data can play a major role in the exploration, identification, and interpretation of as...

Association rules are among the most widely employed data analysis methods in the field of Data Mining. An association rule is a form of partial implication between two sets of binary variables. In the most common approach, association rules are parametrized by a lower bound on their confidence, which is the empirical conditional probability of their consequent given the antecedent, and/or by s...

Association rules are ”if-then rules” with two measures which quantify the support and confidence of the rule for a given data set. Having their origin in market basked analysis, association rules are now one of the most popular tools in data mining. This popularity is to a large part due to the availability of efficient algorithms following from the development of the Apriori algorithm. In the...

The immense volume of web usage data that exists on web servers contains potentially valuable information about the behavior of website visitors. This information can be exploited in various ways, such as enhancing the effectiveness of websites or developing directed web marketing campaigns. In this paper we will focus on applying association rules as a data mining technique to extract potentia...

MOTIVATION We tackle the problem of finding regularities in microarray data. Various data mining tools, such as clustering, classification, Bayesian networks and association rules, have been applied so far to gain insight into gene-expression data. Association rule mining techniques used so far work on discretizations of the data and cannot account for cumulative effects. In this paper, we inve...