Search Results - (( patterns mining algorithm ) OR ((( patterns tree algorithm ) OR ( patterns path algorithm ))))

Search alternatives:

Refine Results
  1. 1
    A Scalable Algorithm for Constructing Frequent Pattern Tree

    A Scalable Algorithm for Constructing Frequent Pattern Tree by Noraziah, Ahmad, Herawan, Tutut, Zailani, Abdullah, Mustafa, Mat Deris

    Published 2014
    “…The construction of FP-Tree is very important prior to frequent patterns mining. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  2. 2
    A frequent pattern mining algorithm based on FP-growth without generating tree

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

    Published 2010
    “…An interesting method to frequent pattern mining without generating candidate pattern is called frequent-pattern growth, or simply FP-growth, which adopts a divide-and-conquer strategy as follows.First, it compresses the database representing frequent items into a frequent-pattern tree, or FP-tree, which retains the itemset association information. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  3. 3
    A frequent pattern mining algorithm based on FP-growth without generating tree

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

    Published 2010
    “…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. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  4. 4
    A numerical method for frequent pattern mining

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

    Published 2009
    “…Frequent pattern mining is one of the active research themes in data mining. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  5. 5
    DFP-growth: An efficient algorithm for mining frequent patterns in dynamic database

    DFP-growth: An efficient algorithm for mining frequent patterns in dynamic database by Zailani, Abdullah, Herawan, Tutut, Noraziah, Ahmad, Mustafa, Mat Deris

    Published 2012
    “…Nowadays, FP-Growth is one of the famous and benchmarked algorithms to mine the frequent patterns from FP-Tree data structure. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  6. 6
    Prime-based method for interactive mining of frequent patterns

    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. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  7. 7
    Using unique-prime-factorization theorem to mine frequent patterns without generating tree

    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. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  8. 8
    An Efficient Data Structure for General Tree-Like Framework in Mining Sequential Patterns Using MEMISP

    An Efficient Data Structure for General Tree-Like Framework in Mining Sequential Patterns Using MEMISP by Saputra, Dhany, Rambli, Dayang R.A., Foong, Oi Mean

    Published 2007
    “…In this paper, we introduce a general tree-like data structure framework for mining sequential patterns. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  9. 9
    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection

    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Dhany, Saputra

    Published 2008
    “…Sequential pattern mining is a new branch of data, mining science that solves inter-transaction pattern mining problems. …”
    Get full text
    Thesis
    Save to List
    Saved in:
  10. 10
    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection

    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

    Published 2008
    “…Sequential pattern mining is a new branch of data, mining science that solves inter-transaction pattern mining problems. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  11. 11
    ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository

    ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository by Zailani, Abdullah, Amir, Ngah, Herawan, Tutut, Noraziah, Ahmad, Siti Zaharah, Mohamad, Abdul Razak, Hamdan

    Published 2017
    “…Therefore, in this paper we proposed a complete model for mining least patterns known as Efficient Least Pattern Mining Model (ELP-M2) with LP-Tree data structure and LP-Growth algorithm. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Book Chapter
    Save to List
    Saved in:
  12. 12
    Frequent Lexicographic Algorithm for Mining Association Rules

    Frequent Lexicographic Algorithm for Mining Association Rules by Mustapha, Norwati

    Published 2005
    “…The algorithm involved the construction of the nodes of a lexicographic tree that represent frequent patterns. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  13. 13
    Compact structure representation in discovering frequent patterns for association rules

    Compact structure representation in discovering frequent patterns for association rules by Mustapha, N., Sulaiman, M.N., Othman, M., Selamat, M.H.

    Published 2002
    “…Flex-tree structure is a lexicographic tree which finds frequent patterns by using depth first search strategy. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  14. 14
    An extended ID3 decision tree algorithm for spatial data

    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. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  15. 15
    Discovering Pattern in Medical Audiology Data with FP-Growth Algorithm

    Discovering Pattern in Medical Audiology Data with FP-Growth Algorithm by G. Noma, Nasir, Mohd Khanapi, Abd Ghani

    Published 2012
    “…We use frequent pattern growth (FP-Growth) algorithm in the data processing step to build the FP-tree data structure and mine it for frequents itemsets. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  16. 16
    Compact structure representation in discovering frequent patterns for association rules

    Compact structure representation in discovering frequent patterns for association rules by Mustapha, Norwati, Sulaiman, Md. Nasir, Othman, Mohamed, Selamat, Mohd Hasan

    Published 2002
    “…Flex-tree structure is a lexicographic tree which finds frequent patterns by using depth first search strategy. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  17. 17
    Ftir of halal and non-halal adulterations prediction analytics using induction decision tree (id3) algorithm

    Ftir of halal and non-halal adulterations prediction analytics using induction decision tree (id3) algorithm by Arief, Akbar, Kartiwi, Mira, Jaswir, Irwandi

    Published 2020
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  18. 18
    Mining Sequential Patterns Using I-PrefixSpan

    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. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  19. 19
    Tracking student performance in introductory programming by means of machine learning

    Tracking student performance in introductory programming by means of machine learning by Khan I., Al Sadiri A., Ahmad A.R., Jabeur N.

    Published 2023
    “…Big data; Decision trees; Education computing; Learning algorithms; Learning systems; Machine learning; Smart city; Students; Trees (mathematics); Educational data mining; Educational institutions; Hidden patterns; Introductory programming; Introductory programming course; Student performance; Student's performance; Weka; Data mining…”
    Conference Paper
    Save to List
    Saved in:
  20. 20
    Improving mining efficiency: A new scheme for extracting association rules

    Improving mining efficiency: A new scheme for extracting association rules by Said, Aiman Moyaid, Dominic, P D D., Abdullah, Azween

    Published 2009
    “…Extracting the association rule from this data is one of the important tasks in data mining.Most of the existing association rules in algorithms typically assume that the data set can fit in the memory.In this paper, we propose a practical and effective scheme to mine association rules from frequent patterns, called Prefixfoldtree scheme (PFT scheme).The original dataset is divided into folds, and then from each fold the frequent patterns are mined by using the tree projection approach.These frequent patterns are combined into one set and finally interestingness constraints are used to extract the association rules.The experiments will be conducted to illustrate the efficiency of our scheme.…”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:

Search Tools: