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

Nguyen, Thanh-Long; Vo, Bay; Huynh, Bao; Snášel, Václav; Nguyen, Loan T. T.

doi:10.1007/978-3-319-67220-5_18

Cited by 3 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the CPCP-Miner [72] algorithm based on CPCP-tree structure was developed to mining colossal patterns with itemset constraint. This algorithm developed a theorem for fast eliminate non-colossal pattern candidates with constraint.…”

Section: Mining Patterns With Constraintsmentioning

confidence: 99%

See 1 more Smart Citation

Efficient algorithms for mining colossal patterns in high dimensional databases

Nguyen

Snášel

2017

Knowledge-Based Systems

Self Cite

View full text Add to dashboard Cite

Section: Mining Patterns With Constraintsmentioning

confidence: 99%

“…In 2017, Nguyen et al proposed a method for mining patterns with the itemset constraint. The authors proposed CPCP-tree for quick mining colossal patterns based on itemset constraint [72].…”

Section: Chapter 5 Mining Colossal Patterns With Length Constraintsmentioning

confidence: 99%

Efficient algorithms for mining colossal patterns in high dimensional databases

Nguyen

Snášel

2017

Knowledge-Based Systems

Self Cite

View full text Add to dashboard Cite

“…Sequential preprocessing approaches used by these present algorithms lead to an exponential upsurge in the mining search area because they do not prune the dataset of all superfluous features and rows. 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.…”

Section: Introductionmentioning

confidence: 99%

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

Sreenivasula Reddy,

Sathya,

Nuka

2024

Contemp. Math.

View full text Add to dashboard Cite

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.

show abstract

Mining colossal patterns with length constraints

Nguyen

Huynh

et al. 2021

Appl Intell

View full text Add to dashboard Cite

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

Cited by 3 publications

References 25 publications

Efficient algorithms for mining colossal patterns in high dimensional databases

Efficient algorithms for mining colossal patterns in high dimensional databases

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

Mining colossal patterns with length constraints

Contact Info

Product

Resources

About