Today, a lot of research is being done to determine the top smart cities in the world. Most of th... more Today, a lot of research is being done to determine the top smart cities in the world. Most of this research work rank the cities on the basis of qualitative analysis. Qualitative methods typically use subjective judgment and not statistical data and therefore, although, they may provide valuable findings, it is difficult to prove the results. Our work is a step towards performing quantitative analysis to rank and compare smart cities. We randomly selected four smart cities and used their open data to figure out if we really have data that can be used to perform quantitative analysis to compare them. We focused on the 'transportation' dimensionality and devised a method to extract information from open data. Our method is also independent of dimensionality and data and therefore can be applied to other dimensionalities to gather an overall picture of the smart cities and perform quantitative research to compare them.
Mining frequent Itemsets has proved to be very difficult because of its computational complexity.... more Mining frequent Itemsets has proved to be very difficult because of its computational complexity. But, , it has gained a lot of popularity due to the usefulness of association rules, despite having huge processing cost. This paper provides a comprehensive survey on the state-of-art algorithms for association rule mining, specially when the datasets used for rule mining are dynamic. When new data are added to a original dataset it may lead to additional rules or to modification of some existing rules. To find the association rules from the whole (old as well as new) dataset will be wastage of time only if the process is restarted from the beginning. Several algorithms have been developed to attend this important issue of the association rule mining problem. This paper analyzes some of the algorithms to tackle the incremental association rule mining problem.
Today, a lot of research is being done to determine the top smart cities in the world. Most of th... more Today, a lot of research is being done to determine the top smart cities in the world. Most of this research work rank the cities on the basis of qualitative analysis. Qualitative methods typically use subjective judgment and not statistical data and therefore, although, they may provide valuable findings, it is difficult to prove the results. Our work is a step towards performing quantitative analysis to rank and compare smart cities. We randomly selected four smart cities and used their open data to figure out if we really have data that can be used to perform quantitative analysis to compare them. We focused on the 'transportation' dimensionality and devised a method to extract information from open data. Our method is also independent of dimensionality and data and therefore can be applied to other dimensionalities to gather an overall picture of the smart cities and perform quantitative research to compare them.
Mining frequent Itemsets has proved to be very difficult because of its computational complexity.... more Mining frequent Itemsets has proved to be very difficult because of its computational complexity. But, , it has gained a lot of popularity due to the usefulness of association rules, despite having huge processing cost. This paper provides a comprehensive survey on the state-of-art algorithms for association rule mining, specially when the datasets used for rule mining are dynamic. When new data are added to a original dataset it may lead to additional rules or to modification of some existing rules. To find the association rules from the whole (old as well as new) dataset will be wastage of time only if the process is restarted from the beginning. Several algorithms have been developed to attend this important issue of the association rule mining problem. This paper analyzes some of the algorithms to tackle the incremental association rule mining problem.
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