Browsing by Author "Gahlot, G."
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Item A pragmatics-oriented high utility mining for itemsets of size two for boosting business yields(Springer Verlag service@springer.de, 2018) Gahlot, G.; Patil, N.Retail market has paced with an enormous rate, sprawling its effect over the nations. The B2C companies have been putting lucrative offers and schemes to fetch the customers’ attractions in the awe of upbringing the business profits, but with the mindless notion of the same. Knowledge discovery in the field of data mining can be well harnessed to achieve the profit benefits. This article proposes the novel way for determining the items to be given on sale, with the logical clubs, thus extending the Apriori algorithm. The dissertation proposes the high-utility mining for itemsets of size two (HUM-IS2) Algorithm using the transactional logs of the superstores. The pruning strategies have been introduced to remove unnecessary formations of the clubs. The essence of the algorithm has been proved by experimenting with various datasets. © Springer Nature Singapore Pte Ltd. 2018.Item Improved speculative Apriori with percentiles algorithm for website restructuring based on usage patterns(2016) Gahlot, G.; Sowmya, Kamath S.Web structure mining techniques are popularly used in the process of improved website design/replanning based on user browsing actions. In this paper, an algorithm for improving the design map (site map of a Website) using the pertinent information available in the website's server logs is proposed, that incorporates probability for extending the well-known Apriori Algorithm. The proposed methodology harnesses the normal distribution curve used in statistical measurements to improve recommendation accuracy after parsing the server log file. This allows the discovery of more association rules as the idea is to use percentile calculations instead of the percentages and having a relative quest within the item sets to determine their existence in the domain. By enforcing the percentile calculations on the distribution curve of the collection, selective items from the small groups within can be obtained. Experimental results for the proposed Speculative Apriori with Percentiles Algorithm (SAwP) indicate that it was effective in discovering relevant itemsets and more association rules, when compared to classical Apriori algorithm. � 2016 IEEE.Item Improved speculative Apriori with percentiles algorithm for website restructuring based on usage patterns(Institute of Electrical and Electronics Engineers Inc., 2016) Gahlot, G.; Kamath S․, S.S.Web structure mining techniques are popularly used in the process of improved website design/replanning based on user browsing actions. In this paper, an algorithm for improving the design map (site map of a Website) using the pertinent information available in the website's server logs is proposed, that incorporates probability for extending the well-known Apriori Algorithm. The proposed methodology harnesses the normal distribution curve used in statistical measurements to improve recommendation accuracy after parsing the server log file. This allows the discovery of more association rules as the idea is to use percentile calculations instead of the percentages and having a relative quest within the item sets to determine their existence in the domain. By enforcing the percentile calculations on the distribution curve of the collection, selective items from the small groups within can be obtained. Experimental results for the proposed Speculative Apriori with Percentiles Algorithm (SAwP) indicate that it was effective in discovering relevant itemsets and more association rules, when compared to classical Apriori algorithm. © 2016 IEEE.Item A pragmatics-oriented high utility mining for itemsets of size two for boosting business yields(2018) Gahlot, G.; Patil, N.Retail market has paced with an enormous rate, sprawling its effect over the nations. The B2C companies have been putting lucrative offers and schemes to fetch the customers� attractions in the awe of upbringing the business profits, but with the mindless notion of the same. Knowledge discovery in the field of data mining can be well harnessed to achieve the profit benefits. This article proposes the novel way for determining the items to be given on sale, with the logical clubs, thus extending the Apriori algorithm. The dissertation proposes the high-utility mining for itemsets of size two (HUM-IS2) Algorithm using the transactional logs of the superstores. The pruning strategies have been introduced to remove unnecessary formations of the clubs. The essence of the algorithm has been proved by experimenting with various datasets. � Springer Nature Singapore Pte Ltd. 2018.