market-basket-analysis | Hadoop MapReduce implementation of Market Basket

by pranitbose Java Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | market-basket-analysis Summary

market-basket-analysis is a Java library typically used in Big Data, Spark, Hadoop applications. market-basket-analysis has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

This Big Data project is a simple working model of Market Basket Analysis. This project is implemented using Hadoop MapReduce framework. Basically this project runs multiple MapReduce jobs to produce the final output. This project uses K-Pass Apriori algorithm for frequent item-sets mining followed by association rule mining to generate all the valid Rules and their corresponding measures such as Support, Confidence and Lift. The frequent item-sets are obtained using a threshold Support and the Rules are validated using a threshold Confidence. Duplicate, reverse and redundant rules are removed to produce interesting and useful rules only. These list of Rules sorted by consequent (RHS of the association) first and then by Lift is the final output of this project. The entire process of building and running this project has been automated using Gradle. Check the Usage section for more details.

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Support

market-basket-analysis has a low active ecosystem.

It has 7 star(s) with 12 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of market-basket-analysis is current.

Quality

market-basket-analysis has 0 bugs and 0 code smells.

Security

market-basket-analysis has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

market-basket-analysis code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

market-basket-analysis is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

market-basket-analysis releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

It has 741 lines of code, 42 functions and 13 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed market-basket-analysis and discovered the below as its top functions. This is intended to give you an instant insight into market-basket-analysis implemented functionality, and help decide if they suit your requirements.

Entry point for the Apriori algorithm
Makes frequent item sets
Add job rules aggregation
Main job to be used
This method reduces the redundant rules
Returns a set of redundant rules
Is a subset of items?
Initialize the list of candidate objects
Generate the next candidate itemsets from the current pass - set
Builds a subset of items from two items - sets
Set up the configuration
Deserialize a Aprior Algorithm object from a file
Write the transaction
Emit each item set in transaction
Reduce key - sets
Map key - value pairs
Write out the header
Reads the number of items from a HDFS file
Performs basic initialization
Reduce the frequent item set
Sort by descending order
Map the value to the key and value
Reduces the number of values in the context

Get all kandi verified functions for this library.

market-basket-analysis Key Features

No Key Features are available at this moment for market-basket-analysis.

market-basket-analysis Examples and Code Snippets

No Code Snippets are available at this moment for market-basket-analysis.

Community Discussions

Trending Discussions on market-basket-analysis

Choosing support and confidence values with ml_fpgrowth in Sparklyr

QUESTION

Choosing support and confidence values with ml_fpgrowth in Sparklyr

Asked 2020-Jan-03 at 10:24

I am trying to take some inspiration from this Kaggle script where the author is using arules to perform a market basket analysis in R. I am particularly interested in the section where they pass in a vector of confidence and support values and then plots the number of rules generated to help chose the optimal values to use rather than generating a massive number of rules.

I wish to try the same process but I am using sparklyr/spark with fpgrowth in R and I am struggling achieve the same output i.e. count of rules for each confidence and support value.

From the limited examples and documentation I believe I pass my transaction data to ml_fpgrowth with my confidence and support values. This function then generates a model which then needs to be passed to ml_association_rules to generate the rules.

...

ANSWER

Answered 2020-Jan-03 at 10:24

After some head banging with dplyr and sparklyr I managed to cobble the following together. If anyone has any feedback as to how I can improve on this code then please feel free to comment.

Source https://stackoverflow.com/questions/59552212

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install market-basket-analysis

You can download it from GitHub.
You can use market-basket-analysis like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the market-basket-analysis component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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