Search Results - (( java implementation modified algorithm ) OR ( knowledge generation patterns algorithm ))
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Direct approach for mining association rules from structured XML data
Published 2012“…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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Thesis -
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A frequent pattern mining algorithm based on FP-growth without generating tree
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A frequent pattern mining algorithm based on FP-growth without generating tree
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Discovering Pattern in Medical Audiology Data with FP-Growth Algorithm
Published 2012“…There is potential knowledge inherent in vast amounts of untapped and possibly valuable data generated by healthcare providers. …”
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Comparison between Market Basket Analysis and Partition Around Medoids clustering for knowledge discovering in consumer consumption pattern / Mohammad Adha Ruslan, Nurul Shahira Mo...
Published 2019“…The main purpose of this study are to compare the knowledge discovery between Market Basket Analysis and Partition Around Medoids and followed by to generate a customer buying pattern by using Market Basket Analysis (MBA) Algorithm and Partition Around Medoids (PAM) Clustering Algorithm. …”
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OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT
Published 2023“…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. The objectives of this work are: i) to provide a comprehensive review of the cloud and scheduling process; ii) to classify the scheduling strategies and scientific workflows; iii) to implement our proposed algorithm with various scheduling algorithms (i.e., Min-Min, Round-Robin, Max-Min, and Modified Max-Min) for performance comparison, within different cloudlet sizes (i.e., small, medium, large, and heavy) in three scientific workflows (i.e., Montage, Epigenomics, and SIPHT); and iv) to investigate the performance of the implemented algorithms by using CloudSim. …”
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Frequent Lexicographic Algorithm for Mining Association Rules
Published 2005“…The primary concept of association rule algorithms consist of two phase procedure. In the first phase, all frequent patterns are found and the second phase uses these frequent patterns in order to generate all strong rules. …”
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Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset
Published 2018“…The multiple variants in the R-Eclat algorithm generate varied performances in infrequent mining patterns. …”
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Prevention And Detection Mechanism For Security In Passive Rfid System
Published 2013“…The proposed protocol is designed with lightweight cryptographic algorithm, including XOR, Hamming distance, rotation and a modified linear congruential generator (MLCG). …”
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Biopattern: a biomimetic design framework for generating bio-inspired design (biomimicry)
Published 2020“…The objective of this research is to develop a biomimetic design framework, BioPattern, which bridges this knowledge gap. BioPattern constitutes of TRIZ, SAPPhIRE, and pattern language. …”
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Automatic generation of content security policy to mitigate cross site scripting
Published 2016“…It can be extended to support generating CSP for contents that are modified by JavaScript after loading. Current approach inspects the static contents of URLs.…”
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Comparative study of apriori-variant algorithms
Published 2016“…However, the algorithm suffers from scanning time problem while generating candidates of frequent itemsets.This study presents a comparative study between several Apriori-variant algorithms and examines their scanning time.We performed experiments using several sets of different transactional data.The result shows that the improved Apriori algorithm manage to produce itemsets faster than the original Apriori algorithm.…”
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An initial state of design and development of intelligent knowledge discovery system for stock exchange database
Published 2004“…Generally our clustering algorithm consists of two steps including training and running steps.The training step is conducted for generating the neural network knowledge based on clustering.In running step, neural network knowledge based is used for supporting the Module in order to generate learned complete data, transformed data and interesting clusters that will help to generate interesting rules.…”
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A Scalable Algorithm for Constructing Frequent Pattern Tree
Published 2014“…In typical FP-Tree construction, besides the prior knowledge on support threshold, it also requires two database scans; first to build and sort the frequent patterns and second to build its prefix paths. …”
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Discovering association rules for mining images datasets: a proposal
Published 2005“…Finally, the association rules will determine using an adaptation of the Apriori Algorithm. The proposed approach is applied to an image datasets to demonstrate the kinds of knowledge and association rules to discover interesting patterns and new knowledge. …”
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Mining dense data: Association rule discovery on benchmark case study
Published 2016“…Data Mining (DM), is the process of discovering knowledge and previously unknown pattern from large amount of data. …”
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Development of intelligent hybrid learning system using clustering and knowledge-based neural networks for economic forecasting : First phase
Published 2004“…We proposed KMeans clustering algorithm that is based on multidimensional scaling, joined with neural knowledge based technique algorithm for supporting the learning module to generate interesting clusters that will generate interesting rules for extracting knowledge from stock exchange databases efficiently and accurately.…”
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Identifying Relationship between Hearing loss Symptoms and Pure-tone Audiometry Thresholds with FP-Growth Algorithm
Published 2013“…The purpose of this study was to find the relationship that exists between pure-tone audiometry threshold values and hearing loss symptoms in a medical datasets of 339 hearing loss patients using association rule mining algorithm. FP-Growth (Frequent Pattern) algorithm is employed for this purpose to generate itemsets given 0.2 (20%) as the support threshold value and 0.7 (70%) as the confidence value for association rule generation. …”
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