A Searching Method of Candidate Segmentation Point in SPRINT Classification

Wang, Wenwen; Wang, Junfang; Huo, Yonghua; Tuo, Yanjun; Yang, Yang

doi:10.1155/2016/2168478

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Data structures were also designed to minimise communication cost which is generated by the movement of some computations to the external storage due to memory limitation [4]. Wang et al, (2016) improved the classical SPRINT algorithm which is a scalable and parallel method of the C4.5 decision tree. The writers came up with a new method for improving the calculation process by looking for a better candidate segmentation point for the discrete and continuous attributes [4].…”

Section: Related Workmentioning

confidence: 99%

Development of an Enhanced C4.5 Decision Tree Algorithm Using a Memoized Mapreduce Model

Paul

2023

Preprint

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Classification technique in data mining focuses on prediction which is done by classical C4.5 decision tree algorithm, but limited by its computation complexities due to large datasets. However, this results to inefficient implementation of the algorithm with reference to computing time, memory utilization and data complexity. Meanwhile, several researches have been done to curb these limitations. One of such improvements is the parallelizing of the algorithm using the MapReduce model. This involves splitting the large dataset into smaller units and distributing them on multiple computers for parallel processing, but the recursive nature of the algorithm makes the computational cost high due to large number of calculations that are repeated. This research is aimed at further reducing computation time, using memoized MapReduce model that involves storing the result of previous calculations in a cache. Thus, when same calculations re-occur, the cached result is returned, thereby eliminating re-computation.

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Section: Related Workmentioning

confidence: 99%

Development of an Enhanced C4.5 Decision Tree Algorithm Using a Memoized Mapreduce Model

Paul

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Data structures were also designed to minimise communication cost which was generated by the movement of some computations to the external storage due to memory limitation. Wang et al, (2016) proposed the classical SPRINT algorithm which was a scalable and parallel method of the C4.5 decision tree. The authors came up with a new method for improving the calculation process by looking for a better candidate segmentation point for the discrete and continuous attributes.…”

Section: Related Workmentioning

confidence: 99%

Development of an Enhanced C4.5 Decision Tree Algorithm Using a Memoized Mapreduce Model

Paul,

Obiniyi,

Donfack-Kana

et al. 2023

FJS

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Classification technique in data mining concentrates on the prediction of categorical or discrete target variables which is designed to be handled by the classical C4.5 decision tree algorithm, an algorithm whose aim is to produce a tree which accurately predicts the target variable for a new unseen data. However its recursive nature poses a limitation when huge volume of dataset is involved; making computation more complex and resulting in an inefficient implementation of the algorithm in terms of computing time, memory utilization and data complexity. Meanwhile, several researches have been done to control these limitations. One of such improvements is the parallelizing of the algorithm using the MapReduce model. This involves dividing the large dataset into smaller units and sharing them on multiple computers for parallel processing, but the recursive nature of the algorithm makes the cost of computing large number of repeated calculations quite high, which is our concern in this work. . This research is aimed at reducing computation time further, by using a memoized MapReduce model, which involves the saving of the result of previous calculations in a cache; hence, when same calculations are encountered again, the cached result is returned, thus re-computation is avoided. The cached result is considered a reduced cost compared to the computational cost of re-computation.

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Data Mining in High-Performance Computing: A Survey of Related Algorithms

Majumder

Chakraborty

2018

Advances in Intelligent Systems and Computing

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A Searching Method of Candidate Segmentation Point in SPRINT Classification

Cited by 3 publications

References 11 publications

Development of an Enhanced C4.5 Decision Tree Algorithm Using a Memoized Mapreduce Model

Development of an Enhanced C4.5 Decision Tree Algorithm Using a Memoized Mapreduce Model

Development of an Enhanced C4.5 Decision Tree Algorithm Using a Memoized Mapreduce Model

Data Mining in High-Performance Computing: A Survey of Related Algorithms

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