Frequent itemset mining_on_hadoop
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This document discusses using Hadoop and the Apriori algorithm for frequent item set mining on big data. Hadoop is a distributed framework that uses MapReduce and HDFS to analyze large datasets across commodity hardware. It is well-suited for frequent item set mining and implementing the Apriori algorithm to efficiently find interesting itemsets and association rules in huge databases with reduced time, cost, and number of scans compared to non-distributed methods.
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Frequent itemset mining_on_hadoop
- 1. What is Hadoop ? Frequent Item Set Mining Apriori Algorithm Why Hadoop for Mining Frequent Item Set ?
- 2. Distributed Framework Used for Analyzing Huge quantity of Data (Big data) Uses Google Map-Reduce Programming Paradigm Works on HDFS (Hadoop Distributed File System) Runs on commodity hardware
- 3. Require for many of the data mining problems Used for Finding interesting itemsets from huge database
- 4. Classical Algorithm for Mining Frequent Itemset Used for mining Association Rules
- 6. Reduce time Reduce Number of Database Scan Works on large Dataset Distributed Framework Reduce Cost Ability to Overcome Failure