H.3.2.15. Transportation
S. Mostafaei; H. Ganjavi; R. Ghodsi
Abstract
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 ...
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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 gasoline consumption. First, the data gathered from different organizations is analyzed by feature selection algorithm to investigate how many of these independent factors have influential effect on the dependent factor. A few of these factors were determined as unimportant and were deleted from the analysis. Two association rule mining algorithms, Apriori and Carma are used to analyze these data. These data which are continuous cannot be handled by these two algorithms. Therefore, the two-step clustering algorithm is used to discretize the data. Association rule mining analysis shows that fewer vehicles, gasoline rationing, and high taxi trips are the main factors that caused low gasoline consumption. Carma results show that the number of taxi trips increase after gasoline rationing. Results also showed that Carma can reach all rules that are achieved by Apriori algorithm. Finaly it showed that association rule mining algorithm results are more informative than statistical correlation analysis.
Farzaneh Zahedi; Mohammad-Reza Zare-Mirakabad
Abstract
Drug addiction is a major social, economic, and hygienic challenge that impacts on all the community and needs serious threat. Available treatments are successful only in short-term unless underlying reasons making individuals prone to the phenomenon are not investigated. Nowadays, there are some treatment ...
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Drug addiction is a major social, economic, and hygienic challenge that impacts on all the community and needs serious threat. Available treatments are successful only in short-term unless underlying reasons making individuals prone to the phenomenon are not investigated. Nowadays, there are some treatment centers which have comprehensive information about addicted people. Therefore, given the huge data sources, data mining can be used to explore knowledge implicit in them, their results can be employed as a knowledge base of decision support systems to make decisions regarding addiction prevention and treatment. We studied participants of such clinics including 471 participants, where 86.2% were male and 13.8% were female. The study aimed to extract rules from the collected data by using association models. Results can be used by rehab clinics to give more knowledge regarding relationships between various parameters and help them for better and more effective treatments. E.g. according to the findings of the study, there is a relationship between individual characteristics and LSD abuse, individual characteristics, the kind of narcotics taken, and committing crimes, family history of drug addiction and family member drug addiction.
