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Microarray data analysis is a very popular topic of current studies in bioinformatics. Most of the existing methods are focused on clustering-related approaches. However, the relations of genes cannot be generated by clustering mining. Some studies explored association rule mining on microarray, but there is no concrete framework proposed on three-dimensional gene-sample-time microarray datasets yet...
This paper describes a model that discovers association rules from a medical database to help doctors treat and diagnose a group of patients who show similar prehistoric medical symptoms. The proposed data mining procedure consists of two modules. The first is a clustering module that is based on a neural network, Adaptive Resonance Theory 2 (ART2), which performs affinity grouping tasks on a large...
Privacy preserving data mining (PPDM) is a novel research direction to preserve privacy for sensitive knowledge from disclosure. Many of the researchers in this area have recently made effort to preserve privacy for sensitive association rules in statistical database. In this paper, we propose a heuristic algorithm named DSRRC (Decrease Support of R.H.S. item of Rule Clusters), which provides privacy...
To gain the competitive advantage in today's age of technology, growing data and to bear the competitive pressure, making strong decisions according to customer's need and market trend has become very important. With huge amount of data on internet, web data mining has become very significant. Web Usage helps companies to produce productive information pertaining to the future of their business function...
The main goal to extract knowledge in database is to help the user to give semantics of data and to optimize the information research. Unfortunately, this fundamental constraint is not taken into account by almost all the approaches for knowledge discovery. Indeed, these approaches generate a big number of rules that are not easily assimilated by the human brain. In this paper, we propose a new approach...
This paper presents an applied study in data mining and knowledge discovery. It aims at discovering patterns within historical students' academic and financial data at UST (University of Science and Technology) from the year 1993 to 2005 in order to contribute improving academic performance at UST. Results show that these rules concentrate on three main issues, students' academic achievements (successes...
Clustering algorithms partition data sets into groups of objects such that the pairwise similarity between objects within the same cluster is higher than those assigned to different clusters. Defining a similarity measure becomes challenging in the presence of categorical data and affects the quality and meaningfulness of the clusters formed. Furthermore, the curse of dimensionality diminishes the...
As data collection and data storage rates are growing at an exponential pace in the business world in this information age, data mining is becoming a key component of electronic commerce. Trends shows definitely that future decision making system in e-commerce would weigh on even quicker and more reliable technology used for data analysis. Confronted with huge mountains of data in business transactions,...
It propose online mining algorithm ( OMA) which online discover large item sets. Without pre-setting a default threshold, the OMA algorithm achieves its efficiency and threshold-flexibility by calculating item-setspsila counts. It is unnecessary and independent of the default threshold and can flexibly adapt to any userpsilas input threshold. In addition, we propose cluster-based association rule...
A new predictive modelling approach known as associative classification, integrating association mining and classification into single system is being discussed as a better alternative for predictive analytics. Our paper investigates the performance issues of significant associative classifiers likes CMAR and CPAR. Performance comparisons observe that CPAR achieves improved performance as compared...
In this paper, we will present an effective Fuzzy Frequent Itemset-Based Hierarchical Clustering (F2IHC) approach, which uses fuzzy frequent itemsets discovered by fuzzy association rule mining to improve the clustering accuracy of FIHC (Frequent Itemset-Based Hierarchical Clustering) method. Our approach can alleviate the deficiencies of most of the traditional document clustering methods in dealing...
This paper analyzed some problems existing in the business search engine when it searched the special fields, and then put forward a set of search engine scheme about data warehouse design based on data mining. It applied the improved association rules algorithm to text clustering algorithm. At the same time in combination with the advantages of J2EE architecture in system development, this paper...
This paper presents a new algorithm called ldquoconcept-groupingrdquo that adapts an association rule mining technique to construct term thesaurus for data preprocessing purpose. Similar terms, which are written differently, can be grouped together into the same concept based on their associations before they are used for subsequent analysis. This data preprocessing is important since it has an impact...
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