Search Results - (( pattern using algorithmic ) OR ((( patterns tree algorithm ) OR ( patterns based algorithm ))))

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  1. 1

    A numerical method for frequent pattern mining by Mustapha, Norwati, Nadimi-Shahraki, Mohammad-Hossein, Mamat, Ali, Sulaiman, Md. Nasir

    Published 2009
    “…The PC_Miner algorithm traverses the PC_Tree by using an efficient pruning technique. …”
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    Article
  2. 2

    Using unique-prime-factorization theorem to mine frequent patterns without generating tree by Tohidi, Hossein, Ibrahim, Hamidah

    Published 2011
    “…In this study we introduce an algorithm to generate frequent patterns without generating a tree and therefore improve the time complexity and memory complexity as well. …”
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    Article
  3. 3

    Prime-based method for interactive mining of frequent patterns by Nadimi-Shahraki, Mohammad-Hossein

    Published 2010
    “…Since rerunning the mining algorithms from scratch can be very time consuming, researchers have introduced interactive mining to find proper patterns by using the current mining model with various minsup. …”
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    Thesis
  4. 4
  5. 5

    Coronary artery segmentation in angiograms with pattern recognition techniques - a survey by Tayebi, Rohollah Moosavi, Sulaiman, Puteri Suhaiza, O. K. Rahmat, Rahmita Wirza, Dimon, Mohd Zamrin, Kadiman, Suhaini, Abdullah, Lilly Nurliyana, Mazaheri, Samaneh

    Published 2013
    “…In this paper, we have divided these algorithms into five major classes and propose a survey for the main class, pattern recognition, which is a famous technique in this manner. …”
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  6. 6

    An extended ID3 decision tree algorithm for spatial data by Sitanggang, Imas Sukaesih, Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin

    Published 2011
    “…It is because spatial data mining algorithms have to consider not only objects of interest itself but also neighbours of the objects in order to extract useful and interesting patterns. …”
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  7. 7

    An empirical study of pattern leakage impact during data preprocessing on machine learning-based intrusion detection models reliability by Bouke, Mohamed Aly, Abdullah, Azizol

    Published 2023
    “…In this paper, we investigate the impact of pattern leakage during data preprocessing on the reliability of Machine Learning (ML) based intrusion detection systems (IDS). …”
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    Article
  8. 8

    Pattern generation through feature values modification and decision tree ensemble construction by Akhand, M. A. H, Rahman, M.M. Hafizur, Murase, K.

    Published 2013
    “…The method modifies feature values of some patterns with the values of other patterns to generate different patterns for different classifiers. …”
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    Article
  9. 9

    Evaluation of fall detection classification approaches by Kerdegari, Hamideh, Samsudin, Khairulmizam, Ramli, Abdul Rahman, Ghotoorlar, Saeid Mokaram

    Published 2012
    “…This paper presents the comparison of different machine learning classification algorithms using Waikato Environment for Knowledge Analysis (WEKA) platform for classifying falling patterns from ADL patterns. …”
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  10. 10

    Disparity map algorithm using hierarchical of bitwise pixel differences and segment-tree from stereo image by Zainal Azali, Muhammad Nazmi

    Published 2024
    “…This thesis presents a local-based stereo matching algorithm to increase the accuracy on complex regions. …”
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    Thesis
  11. 11

    Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant by Morshidi, Malik Arman

    Published 2007
    “…In this research, a vision system algorithm has been developed to identify and locate base of young corn trees based upon robot vision technology, pattern recognition techniques, and knowledge-based decision theory. …”
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    Thesis
  12. 12

    Finger Motion In Classifying Offline Handwriting Patterns by Yeoh, Shen Horng

    Published 2017
    “…In previous studies, the offline handwriting classification is determined solely based on the handwriting patterns. To the best of our knowledge, no studies were found to predict the English words inclination based on the finger motions. …”
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    Monograph
  13. 13

    A hybrid interpretable deep structure based on adaptive neuro‑fuzzy inference system, decision tree, and K‑means for intrusion detection by Jia, Lu, Yin Chai, Wang, Chee Siong, Teh, Xinjin, Li, Liping, Zhao, Fengrui, Wei

    Published 2022
    “…For generating an interpretable deep architecture for identifying deep intrusion patterns, this study proposes an approach that combines ANFIS (Adaptive Network-based Fuzzy Inference System) and DT (Decision Tree) for interpreting the deep pattern of intrusion detection. …”
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    Article
  14. 14
  15. 15

    Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

    Published 2008
    “…In this paper, we propose an improvement of pattern growth-based PrefixSpan algorithm, called I-PrefixSpan. …”
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    Citation Index Journal
  16. 16

    Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud by Shariff, S. Sarifah Radiah, Hud, Hady

    Published 2023
    “…The unpredictability of customers’ purchasing patterns postpandemic has rendered heuristic-based forecasting large forecast errors. …”
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    Book Section
  17. 17

    Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

    Published 2007
    “…Most of the previous works explore Apriori approach which is not efficient in mining plentiful and long patterns. In this paper, we propose an improvement of pattern growth-based PrefixSpan algorithm, called I-PrefixSpan. …”
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  18. 18

    An efficient model for indoor radio signal prediction and coverage estimation / Md. Sumon Sarker by Md. Sumon, Sarker

    Published 2011
    “…Moreover, it is also revealed that the proposed coverage algorithm is capable of reducing the computation time as high as 99% because of strong bounding functions as well as the concept of magnificent coverage pattern.…”
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    Thesis
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  20. 20

    A frequent pattern mining algorithm based on FP-growth without generating tree by Tohid, Hossein, Ibrahim, Hamidah

    Published 2010
    “…It then divides the compressed database into a set of conditional databases (a special kind of projected database), each associated with one frequent item or pattern fragment, and mines each such database separately.For a large database, constructing a large tree in the memory is a time consuming task and increase the time of execution.In this paper we introduce an algorithm to generate frequent patterns without generating a tree and therefore improve the time complexity and memory complexity as well.Our algorithm works based on prime factorization, and is called Frequent Pattern- Prime Factorization (FPPF).…”
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