In this paper, the relation among factors in the road transportation sector from March, 2005 to March, 2011 is analyzed. Most of the previous studies have economical point of view on gasoline consumption. Here, a new approach is proposed in which different data mining techniques are used to extract meaningful relations between the aforementioned factors. The main and dependent factor is gasolin...

We consider a supervised learning problem in which data are revealed sequentially and the goal is to determine what will next be revealed. In the context of this problem, algorithms based on association rules have a distinct advantage over classical statistical and machine learning methods; however, there has not previously been a theoretical foundation established for using association rules i...

Data mining is used to extract useful information hidden in the data. Sometimes this extraction of information leads to revealing sensitive information. Privacy preservation in Data Mining is a process of sanitizing sensitive information. This research focuses on sanitizing sensitive rules discovered in quantitative data. The proposed scheme, Privacy Preserving in Fuzzy Association Rules (PPFAR...

In this paper we propose a new technique to select the top „n‟ association rules out of a pool of „k‟ association rules based on heuristic analysis. The proposed method ranks association rules giving emphasis to a larger set of parameters than used by standard methods. The role of correlation has been emphasized in the proposed method which also tries to eliminate issues faced in incorporating ...

The Human Phenotype Ontology (HPO) is a structured repository of concepts (HPO Terms) that are associated to one or more diseases. The process of association is referred to as annotation. The relevance and the specificity of both HPO terms and annotations are evaluated by a measure defined as Information Content (IC). The analysis of annotated data is thus an important challenge for bioinformat...

We present theoretical analysis and a suite of tests and procedures for addressing a broad class of redundant and misleading association rules we call specious rules. Specious dependencies, also known as spurious, apparent, or illusory associations, refer to a well-known phenomenon where marginal dependencies are merely products of interactions with other variables and disappear when conditione...

ABSTRACT: Association Rule Mining (AM) is one of the most popular data mining techniques. Association rule mining generates a large number of rules based on support and confidence. However, post analysis is required to obtain interesting rules as many of the generated rules are useless.However, the size of the database can be very large. It is very time consuming to find all the association rul...

The mining association algorithm is one of the most important data mining algorithms to derive association rules at high speed from huge databases. However, the algorithm tends to derive those rules that contain noises such as stopwords then some systems remove the noises using noise filters. We have been improving the algorithm and developing navigation systems for semi-structured data using t...