A Study on Improved Eclat Data Mining Algorithm

2011 ◽  
Vol 328-330 ◽  
pp. 1896-1899 ◽  
Author(s):  
Zhi Fang Li ◽  
Xiu Fang Liu ◽  
Xu Cao
Keyword(s):  
Data Mining ◽  
Data Mining Algorithm ◽  
Advantages And Disadvantages ◽  
Mining Algorithm

An introduction on the algorithm Apriori and FP-growth is given. And their advantages and disadvantages are pointed out. The rule of mining transaction databases has two common formats, horizontal layout and vertical layout. Normally, algorithm using vertical database layout is often superior to those using horizontal layout. A new Eclat algorithm was brought out, which is an improvement of Eclat and show good performance with a lot of datasets.

scholarly journals Analysis of the Impact of Big Data on E-Commerce in Cloud Computing Environment

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Rongrui Yu ◽  
Chunqiong Wu ◽  
Bingwen Yan ◽  
Baoqin Yu ◽  
Xiukao Zhou ◽  
...  
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Data Storage ◽  
Large Scale ◽  
Query Language ◽  
Optimal Path ◽  
Data Mining Algorithm ◽  
Advantages And Disadvantages ◽  
Mining Algorithm ◽  
The Impact

This article starts with the analysis of the existing electronic commerce system, summarizes its characteristics, and analyzes and solves its existing problems. Firstly, the characteristics of the relational database My Structured Query Language (MySQL) and the distributed database HBase are analyzed, their respective advantages and disadvantages are summarized, and the advantages and disadvantages of each are taken into account when storing data. My SQL is used to store structured business data in the system, while HBase is used to store unstructured data such as pictures. These two storage mechanisms together constitute a data storage subsystem. Secondly, considering the large amount of data in the e-commerce system and the complex calculation of the data mining algorithm, this paper uses MapReduce to realize the parallelization of the data mining algorithm and builds a Hadoop-based commodity recommendation subsystem on this basis. We use JavaEE technology to design a full-featured web mall system. Finally, based on the impact of cloud computing, mobile e-commerce is analyzed, including relevant theories, service mode, architecture, core technology, and the application in e-commerce, which can realize e-commerce precision marketing, find the optimal path of logistics, and take effective security measures to avoid transaction risks. This method can avoid the disadvantages of the traditional e-commerce, where large-scale data cannot be processed in a timely manner, realize the value of mining data behind, and realize the precision marketing of e-commerce enterprises.

scholarly journals A product-centric data mining algorithm for targeted promotions

2020 ◽  
Vol 54 ◽  
pp. 101940 ◽  
Author(s):  
Raymond Moodley ◽  
Francisco Chiclana ◽  
Fabio Caraffini ◽  
Jenny Carter
Keyword(s):  
Data Mining ◽  
Data Mining Algorithm ◽  
Mining Algorithm

scholarly journals Life-cycle Asset Management in Residential Developments Building on Transport System Critical Attributes via a Data-mining Algorithm

Buildings ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 1 ◽  
Author(s):  
Umair Hasan ◽  
Andrew Whyte ◽  
Hamad Al Jassmi
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Built Environment ◽  
Transport System ◽  
Asset Management ◽  
Public Transport ◽  
Categorical Variables ◽  
Data Mining Algorithm ◽  
Large Set ◽  
Mining Algorithm

Public transport can discourage individual car usage as a life-cycle asset management strategy towards carbon neutrality. An effective public transport system contributes greatly to the wider goal of a sustainable built environment, provided the critical transit system attributes are measured and addressed to (continue to) improve commuter uptake of public systems by residents living and working in local communities. Travel data from intra-city travellers can advise discrete policy recommendations based on a residential area or development’s public transport demand. Commuter segments related to travelling frequency, satisfaction from service level, and its value for money are evaluated to extract econometric models/association rules. A data mining algorithm with minimum confidence, support, interest, syntactic constraints and meaningfulness measure as inputs is designed to exploit a large set of 31 variables collected for 1,520 respondents, generating 72 models. This methodology presents an alternative to multivariate analyses to find correlations in bigger databases of categorical variables. Results here augment literature by highlighting traveller perceptions related to frequency of buses, journey time, and capacity, as a net positive effect of frequent buses operating on rapid transit routes. Policymakers can address public transport uptake through service frequency variation during peak-hours with resultant reduced car dependence apt to reduce induced life-cycle environmental burdens of buildings by altering residents’ mode choices, and a potential design change of buildings towards a public transit-based, compact, and shared space urban built environment.

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