Efficient algorithms for mining colossal patterns in high dimensional databases

Nguyen, Thanh-Long; Vo, Bay; Snášel, Václav

doi:10.1016/j.knosys.2017.01.034

Cited by 16 publications

(2 citation statements)

References 62 publications

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“…An algorithm termed Pattern-Fusion [8] was developed to mine colossal itemset by skipping small cardinality itemsets which are less useful for scientists. Since then, several colossal (closed) itemset mining algorithm were developed in the effort to discover useful and interesting knowledge from high-dimensional data, including CPM [9], DPMine [10], BVBUC [11], DisClose [12] and CP-Miner [13]. CPM, DPMine, DisClose and CP-Miner store the discovered colossal closed itemsets in a prefix tree, while BVBUC uses a text file which resulted to longer running time to check for duplicates.…”

Section: Closed Frequent Itemset Mining Methodsmentioning

confidence: 99%

Improved BVBUC Algorithm to Discover Closed Itemsets in Long Biological Datasets

Zaki

Zulkurnain

2019

AMM

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The task in mining closed frequent itemsets requires the algorithm to mine the frequent ones then determine its closure. The efficiency of closure computation is very important as it will determine the total mining time and the required memory. Over the years, many closure computation methods have been proposed to achieve these goals. However, to the best of our knowledge, there is no suitable method that can be adapted for algorithms that enumerate the rowset lattice, which is effective for biological datasets. Therefore, this paper proposed a method for computing closure compare with the method used in BVBUC algorithm method. Finally, BVBUC_I is proposed and the performances of these algorithms were evaluated using two synthetic datasets and three real datasets. The results of these tests proved the efficiency of the proposed method.

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Section: Closed Frequent Itemset Mining Methodsmentioning

confidence: 99%

Improved BVBUC Algorithm to Discover Closed Itemsets in Long Biological Datasets

Zaki

Zulkurnain

2019

AMM

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“…Mining only a subset of FCCI leads to a lessthan-complete set of association rules, which can have a chilling effect on your ability to make sound judgments [12,13]. There is a high probability that FCCI extracted using the bit-wise vertical bottom up colossal (BVBUC) pattern mining approach will yield inaccurate supporting data [14][15][16][17], which in turn will result in an incorrect set of association rules that will have an impact on the quality of the decisions made. Existing algorithms' ineffective closeness checking and trimming approaches make it difficult to narrow the mining search space.…”

Section: Introductionmentioning

confidence: 99%

Mining the High Dimensional Biological Dataset Using Optimized Colossal Pattern with Dimensionality Reduction

Sreenivasula Reddy,

Sathya,

Nuka

2024

Contemp. Math.

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Recent years have seen a lot of attention paid to the mining of enormous item sets from high-dimensional databases. Small and mid-sized data sets take a long time to mine with traditional algorithms since they don’t include the complete and relevant info needed for decision making. Many applications, particularly in bioinformatics, benefit greatly from the extraction of (FCCI) Frequent Colossal Closed Itemsets from a large dataset. In order to extract FCCI from a dataset, present preprocessing strategies fail to remove all extraneous characteristics and rows from the data set completely. In addition, the most current algorithms for this kind are sequential and computationally expensive. A high-dimensional dataset is pruned of all extraneous characteristics and rows using two alternative dimensionality reduction strategies presented in this paper. Then, an optimal feature value is identified by using Equilibrium Optimizer (EO) to identify the threshold value for reduced features. It is designed to discover common items and build association rules if the feature value is smaller than the frequency mining algorithm (IFRS) in conjunction with the Fruit fly Algorithm (FFA). If the feature value exceeds the optimal threshold, then optimized Length restrictions can be used to solve the CP mining problem (LC). Random search is utilized to identify the optimal threshold values of the restrictions and extract the enormous pattern using the Differential Evolutionary Arithmetic Optimization Algorithm. The experiments are carried on twenty biological datasets that us extracted from UCI websites and validated the proposed models in terms of various metrics.

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Constraint-Based Method for Mining Colossal Patterns in High Dimensional Databases

Nguyen

Huynh

et al. 2017

Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architectu

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Efficient algorithms for mining colossal patterns in high dimensional databases

Cited by 16 publications

References 62 publications

Improved BVBUC Algorithm to Discover Closed Itemsets in Long Biological Datasets

Improved BVBUC Algorithm to Discover Closed Itemsets in Long Biological Datasets

Mining the High Dimensional Biological Dataset Using Optimized Colossal Pattern with Dimensionality Reduction

Constraint-Based Method for Mining Colossal Patterns in High Dimensional Databases

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