Fast Frequent Item Mining from Big Data using Map Reduce and Bit Vectors
Main Article Content
Abstract
One of the most important areas that are constantly being focused recently is the big data and mining frequent patterns from them is an interesting vertical which is perpetually being evolved and gained plethora of attention among the research fraternities. Generally, the data is mined with the aid of Apriori based algorithms, tree based algorithm and hash based algorithm but most of these existing algorithms suffer many snags and limitations. This paper proposes a new method that overrides and overcomes the most common problems related to speed, memory consumption and search space. The algorithm named Dual Mine employs binary vector representation and vertical data representations in the map reduce and then discover the most patterns from the large data sets. The Dual mine algorithm is then compared with some of the existing algorithms to determine the efficiency of the proposed algorithm and from the experimental results it is quite evident that the proposed algorithm “Dual Mine” outscored the other algorithms by a big magnitude with respect to speed and memory.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.