Unified Framework for Data Mining using Frequent Model Tree
Clicks: 304
ID: 13845
2016
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
69.1
/100
304 views
248 readers
Trending
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #13 of 148 articles by views in adbu journal of engineering technology
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 148 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Abstract: Data mining is the science of discovering hidden patterns from data. Over the past years, a plethora of data mining algorithms has been developed to carry out various data mining tasks such as classification, clustering, association mining and regression. All the methods are ad-hoc in nature, and there exists no unifying framework which unites all the data mining tasks. This study proposes such a framework which describes a data modelling technique to model data in a manner that can be used to accomplish all kinds of data mining tasks. This study proposed a novel algorithm known as Frequent Model (FM)-Growth, based on Frequent pattern (FP)-Growth algorithm. The algorithm is used to find frequent patterns or models from data. These models will then be used to carry out various data mining tasks such as classification, clustering. The advantage of these frequent models is that they can be used as it is with any data mining task irrespective of the nature of the task. The algorithm is carried out in two stages. In the first stage, we grow the FM-tree from the data and in the second stage, we extract the frequent models from the FM-tree. The accuracy of the proposed algorithm is high. However, the algorithm is computationally expensive when searching for frequent models in high volume and high dimensional data. The reason of expensiveness is that it needs to travel all the nodes of a tree. The study suggests measures to be taken to improve the efficiency of the overall process using dictionary data structure. Keywords: Data Mining, Frequent Pattern Recognition Unified Framework, Classification, Clustering, FPGrowth tree.
| Reference Key |
singh2016unifiedadbu
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Shahid Zaman, Yumnam Jayanta Singh; |
| Journal | adbu journal of engineering technology |
| Year | 2016 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.