Improvised Apriori Algorithm Using Frequent Pattern Tree for Real Time Applications in Data Mining
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TLDR
The limitation of the original Apriori algorithm of wasting time and space for scanning the whole database searching on the frequent itemsets, and an improvement on A Priori are indicated.About:
This article is published in Procedia Computer Science.The article was published on 2015-01-01 and is currently open access. It has received 75 citations till now. The article focuses on the topics: Apriori algorithm & Association rule learning.read more
Citations
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Journal ArticleDOI
Cloud-centric IoT based disease diagnosis healthcare framework
Prabal Verma,Sandeep K. Sood +1 more
TL;DR: A cloud-centric IoT basedm-healthcare monitoring disease diagnosing framework is proposed which predicts the potential disease with its level of severity and experimental results show that the proposed methodology outperforms the baseline methods for disease prediction.
Journal ArticleDOI
Application of an improved Apriori algorithm in a mobile e-commerce recommendation system
Yan Guo,Minxi Wang,Xin Li +2 more
TL;DR: The results of the experimental study clearly show that the mobile e-commerce recommendation system based on an improved Apriori algorithm increases the efficiency of data mining to achieve the unity of real time and recommendation accuracy.
Proceedings ArticleDOI
An improved Apriori algorithm for mining association rules
TL;DR: Under the same conditions, the results illustrate that the proposed improved Apriori algorithm improves the operating efficiency compared with other improved algorithms.
Journal ArticleDOI
Energy efficient clustering with disease diagnosis model for IoT based sustainable healthcare systems
R. Bharathi,T. Abirami,S. Dhanasekaran,Deepak Gupta,Ashish Khanna,Mohamed Elhoseny,Mohamed Elhoseny,K. Shankar +7 more
TL;DR: An Energy Efficient Particle Swarm Optimization (PSO) based Clustering (EEPSOC) technique for the effective selection of cluster heads (CHs) among diverse IoT devices and an artificial neural network (ANN) based classification model is applied.
References
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Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Proceedings ArticleDOI
Mining association rules between sets of items in large databases
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
Journal ArticleDOI
Top 10 algorithms in data mining
Xindong Wu,Vipin Kumar,J. Ross Quinlan,Joydeep Ghosh,Qiang Yang,Hiroshi Motoda,Geoffrey J. McLachlan,Angus S. K. Ng,Bing Liu,Philip S. Yu,Zhi-Hua Zhou,Michael Steinbach,David J. Hand,Dan Steinberg +13 more
TL;DR: This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
Journal ArticleDOI
Mining association rules between sets of items in large databases
TL;DR: An efficient algorithm is presented that generates all significant transactions in a large database of customer transactions that consists of items purchased by a customer in a visit.