Mining colossal patterns with length constraints

Le, Tuong; Nguyen, Thanh-Long; Huynh, Bao; Nguyen, Hung T.; Hong, Tzung-Pei; Snåšel, Václav

doi:10.1007/s10489-021-02357-8

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…Length constraint (LC) tree layout for frequent item collections was also presented by Yun et al [21]. It has recently been proposed that the algorithms dubbed PCP-Miner [22] be used to mine massive patterns using the -core ratio. There are no supersets for a -core pattern in the database; hence, it is considered enormous.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…Furthermore, other approaches with which to mine patterns in those cases in which the amount of data is limited have recently been presented. For example, the authors of [27] show an algorithm that can be used to mine colossal patterns, i.e., patterns extracted from databases with many attributes and values, but with few instances.…”

Section: Related Workmentioning

confidence: 99%

VLSD—An Efficient Subgroup Discovery Algorithm Based on Equivalence Classes and Optimistic Estimate

Lopez-Martinez-Carrasco

Juárez

Campos

et al. 2023

Algorithms

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Subgroup Discovery (SD) is a supervised data mining technique for identifying a set of relations (subgroups) among attributes from a dataset with respect to a target attribute. Two key components of this technique are (i) the metric used to quantify a subgroup extracted, called quality measure, and (ii) the search strategy used, which determines how the search space is explored and how the subgroups are obtained. The proposal made in this work consists of two parts, (1) a new and efficient SD algorithm which is based on the equivalence class exploration strategy, and which uses a pruning based on optimistic estimate, and (2) a data structure used when implementing the algorithm in order to compute subgroup refinements easily and efficiently. One of the most important advantages of this algorithm is its easy parallelization. We have tested the performance of our SD algorithm with respect to some other well-known state-of-the-art SD algorithms in terms of runtime, max memory usage, subgroups selected, and nodes visited. This was completed using a collection of standard, well-known, and popular datasets obtained from the relevant literature. The results confirmed that our algorithm is more efficient than the other algorithms considered.

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“…Frequent itemsets have also been applied to solvethe problem of multi-attribute users under conditions of local differential privacy [5]. There are also numerous variations of pattern mining, including frequent closed itemset mining [6], maximal frequent patterns [7], frequent weighted itemset mining [8], utility patterns [9,10], colossal patterns [11], erasable itemset mining [12], and so on. These variations have different meanings and are used in intelligent systems for specific situations.…”

Section: Introductionmentioning

confidence: 99%

A Fast Algorithm for Mining Top-Rank-k Erasable Closed Patterns

Nguyen¹,

Le²

2022

Computers, Materials &Amp; Continua

Self Cite

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The task of mining erasable patterns (EPs) is a data mining problem that can help factory managers come up with the best product plans for the future. This problem has been studied by many scientists in recent times, and many approaches for mining EPs have been proposed. Erasable closed patterns (ECPs) are an abbreviated representation of EPs and can be considered condensed representations of EPs without information loss. Current methods of mining ECPs identify huge numbers of such patterns, whereas intelligent systems only need a small number. A ranking process therefore needs to be applied prior to use, which causes a reduction in efficiency. To overcome this limitation, this study presents a robust method for mining toprank-k ECPs in which the mining and ranking phases are combined into a single step. First, we propose a virtual-threshold-based pruning strategy to improve the mining speed. Based on this strategy and dPidset structure, we then develop a fast algorithm for mining top-rank-k ECPs, which we call TRK-ECP. Finally, we carry out experiments to compare the runtime of our TRK-ECP algorithm with two algorithms modified from dVM and TEPUS (Top-rank-k Erasable Pattern mining Using the Subsume concept), which are state-of-the-art algorithms for mining top-rank-k EPs. The results for the running time confirm that TRK-ECP outperforms the other experimental approaches in terms of mining the top-rank-k ECPs.

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Mining colossal patterns with length constraints

Cited by 5 publications

References 32 publications

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

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

VLSD—An Efficient Subgroup Discovery Algorithm Based on Equivalence Classes and Optimistic Estimate

A Fast Algorithm for Mining Top-Rank-k Erasable Closed Patterns

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