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Frequent Word Combinations Mining and Indexing on HBase. Hemanth Gokavarapu Santhosh Kumar Saminathan. Introduction. Many projects use Hbase to store large amount of data for distributed computation

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frequent word combinations mining and indexing on hbase

Frequent Word Combinations Mining and Indexing on HBase


Santhosh Kumar Saminathan

  • Many projects use Hbase to store large amount of data for distributed computation
  • The Processing of these data becomes a challenge for the programmers
  • The use of frequent terms help us in many ways in the field of machine learning
  • Eg: Frequently purchased items, Frequently Asked Questions, etc.
  • These projects on Hbase create indexes on multiple data
  • We are able to find the frequency of a single word easily using these indexes
  • It is hard to find the frequency of a combination of words
  • For example: “cloud computing”
  • Searching these words separately may lead to results like “scientific computing”, “cloud platform”
  • This project focuses on finding the frequency of a combination of words
  • We use the concept of Data mining and Apriori algorithm for this project
  • We will be using Map-Reduce and HBase for this project.
survey topics
Survey Topics
  • Apriori Algorithm
  • HBase
  • Map – Reduce
data mining
Data Mining

What is Data Mining?

  • Process of analyzing data from different perspective
  • Summarizing data into useful information.
data mining1
Data Mining

How Data Mining works?

  • Data Mining analyzes relationships and patterns in stored transaction data based on open – ended user queries

What technology of infrastructure is needed?

Two critical technological drivers answers this question.

  • Size of the database
  • Query complexity
apriori algorithm
Apriori Algorithm
  • Apriori Algorithm – Its an influential algorithm for mining frequent item sets for Boolean association rules.
  • Association rules form an very applied data mining approach.
  • Association rules are derived from frequent itemsets.
  • It uses level-wise search using frequent item property.
apriori algorithm problem description
Apriori Algorithm & Problem Description

If theminimum support is 50%, then {Shoes, Jacket} is the only 2- itemset that satisfies the minimum support.

If the minimum confidence is 50%, then the only two rules generated from this 2-itemset, that have confidence greater than 50%, are:

Shoes  Jacket Support=50%, Confidence=66%

Jacket  Shoes Support=50%, Confidence=100%

apriori algorithm example
Database D



Scan D




Scan D



Scan D

Apriori Algorithm Example

Min support =50%

apriori advantages disadvantages
Apriori Advantages & Disadvantages

Uses larger itemset property

Easily Parallelized

Easy to Implement


Assumes transaction database is memory resident

Requires many database scans


What is HBase?

  • A Hadoop Database
  • Non - Relational
  • Open-source, Distributed, versioned, column-oriented store model
  • Designed after Google Bigtable
  • Runs on top of HDFS ( Hadoop Distributed File System )
map reduce
Map Reduce
  • Framework for processing highly distributable problems across huge datasets using large number of nodes. / cluster.
  • Processing occur on data stored either in filesystem ( unstructured ) or in Database ( structured )
mapper and reducer
Mapper and Reducer
  • Mappers
      • FreqentItemsMap
      • -Finds the combination and assigns the key value for each combination
      • CandidateGenMap
      • AssociationRuleMap
  • Reducer
        • FrequentItemsReduce
        • CandidateGenReduce
        • AssociationRuleReduce
flow chart
Flow Chart


Find Frequent Items

Find Candidate Itemsets

Find Frequent Items


Set Null?


Generate Association Rules

  • 1 week – Talking to the Experts at Futuregrid
  • 1 Week – survey of HBase, Apriori Algorithm
  • 4 Weeks -- Kick start on implementing Apriori Algorithm
  • 2 Weeks – Testing the code and get the results.
  • The execution takes more time for the single node
  • As the number of mappers getting increased, we come up with better performance
  • When the data is very large, single node execution takes more time and behaves weirdly
known issues
Known Issues
  • When the frequency is very low for large data set the reducer takes more time
  • Eg: A text paragraph in which the words are not repeated often.
future work
Future Work
  • The analysis can be done with Twister and other platform
  • The algorithm can be extended for other applications that use machine learning techniques