Main Reference PaperOptimum Frequent Pattern Approach for Efficient Incremental Mining on Large Databases using Map Reduce, International Journal of Computer Applications, June 2015
  • Frequent itemset mining is used to gather item sets after discovering association rules. Some limitations exist with the traditional association rule mining algorithms for large-scale data. To overcome these drawbacks, this work implements an optimum pattern Tree with the node as the data item of the transaction. The process of adding the item set is also done in an incremental manner as the property of the optimum frequent pattern. This kind of approach is finding best frequent pattern in large dataset.

+ Description
  • Frequent itemset mining is used to gather item sets after discovering association rules. Some limitations exist with the traditional association rule mining algorithms for large-scale data. To overcome these drawbacks, this work implements an optimum pattern Tree with the node as the data item of the transaction. The process of adding the item set is also done in an incremental manner as the property of the optimum frequent pattern. This kind of approach is finding best frequent pattern in large dataset.

  • Reducing computation cost.

  • Reducing memory consumption.

  • To achieve scalability.

+ Aim & Objectives
  • Reducing computation cost.

  • Reducing memory consumption.

  • To achieve scalability.

  • A technique of FP-growth is integrated with proposed scheme to find the best frequent pattern and reduce the memory consumptionfurther.

+ Contribution
  • A technique of FP-growth is integrated with proposed scheme to find the best frequent pattern and reduce the memory consumptionfurther.

  • Java JDK 1.8, MySQL 5.5.40, Hadoop 1.2.1.

  • Netbeans 8.0.1, J2EE, Hadoop.

+ Software Tools & Technologies
  • Java JDK 1.8, MySQL 5.5.40, Hadoop 1.2.1.

  • Netbeans 8.0.1, J2EE, Hadoop.

  • B.E / B.Tech / M.E / M.Tech

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

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