Main Reference PaperEfficient Algorithms for Mining High Utility Item sets from Transactional Databases, IEEE Transactions on Knowledge and Data Engineering, Aug 2013
  • This paper proposes two algorithms, namely utility pattern growth (UP-Growth) and UP-Growth+, for mining high utility itemsets with a set of effective strategies for pruning candidate itemsets. The information of high utility itemsets is maintained in a tree-based data structure named utility pattern tree (UP-Tree) such that candidate itemsets can be generated efficiently with only two scans of database. The performance of UP-Growth and UP-Growth+ is compared with the state-of-the-art algorithms on many types of both real and synthetic data sets. Experimental results show that the proposed algorithms, especially UP-Growth+, not only reduce the number of candidates effectively but also outperform other algorithms substantially in terms of runtime, especially when databases contain lots of long transactions

+ Description
  • This paper proposes two algorithms, namely utility pattern growth (UP-Growth) and UP-Growth+, for mining high utility itemsets with a set of effective strategies for pruning candidate itemsets. The information of high utility itemsets is maintained in a tree-based data structure named utility pattern tree (UP-Tree) such that candidate itemsets can be generated efficiently with only two scans of database. The performance of UP-Growth and UP-Growth+ is compared with the state-of-the-art algorithms on many types of both real and synthetic data sets. Experimental results show that the proposed algorithms, especially UP-Growth+, not only reduce the number of candidates effectively but also outperform other algorithms substantially in terms of runtime, especially when databases contain lots of long transactions

  • To generate the minimum number of candidate sets with high utility.

  • To reduce the database scanning.

  • To produce the UP growth trees with the high utility item sets.

  • To Find Potential High Utility item sets.

  • To improve the scalability

+ Aim & Objectives
  • To generate the minimum number of candidate sets with high utility.

  • To reduce the database scanning.

  • To produce the UP growth trees with the high utility item sets.

  • To Find Potential High Utility item sets.

  • To improve the scalability

  • This paper contributes the method to find high profit item set from UP growth .It finds high utility item sets from UP Growth tree. To find highly profitable items set ,it uses UP-growth+ algorithm to find highly profitable items from the database. It increases the scalability.

+ Contribution
  • This paper contributes the method to find high profit item set from UP growth .It finds high utility item sets from UP Growth tree. To find highly profitable items set ,it uses UP-growth+ algorithm to find highly profitable items from the database. It increases the scalability.

  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1 and J2SE

+ Software Tools & Technologies
  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1 and J2SE

  • B.E / B.Tech / M.E / M.Tech

+ Project Recommended For
  • B.E / B.Tech / M.E / M.Tech

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