A Fast Approach for Up-Scaling Frequent Itemsets

Chen, Runzi; Zhao, Shuliang; Liu, Mengmeng

doi:10.1109/access.2020.2995719

Cited by 7 publications

(7 citation statements)

References 23 publications

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“…Clustering is one of the vital data mining and machine learning techniques, which aims to group similar objects into the same cluster and separate dissimilar objects into different clusters [ 1 ]. It is so prominent and recently attracted significant attention of researchers and practitioners from different domains of science and engineering [ 2 ]. Thousands of papers had been published [ 3 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…The scale can be equivalent to the following concept: generic concept, level of abstraction, or perspective of observation; the same problem or system can be perceived at different scales based on particular needs [ 2 ]. That is called multiscale phenomena and has been widely applied to the academic fields, such as geoscience [ 3 , 4 ] and mathematics [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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A Multiscale Clustering Approach for Non‐IID Nominal Data

Chen

Zhao

Tian

2021

Computational Intelligence and Neuroscience

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Multiscale brings great benefits for people to observe objects or problems from different perspectives. Multiscale clustering has been widely studied in various disciplines. However, most of the research studies are only for the numerical dataset, which is a lack of research on the clustering of nominal dataset, especially the data are nonindependent and identically distributed (Non-IID). Aiming at the current research situation, this paper proposes a multiscale clustering framework based on Non-IID nominal data. Firstly, the benchmark-scale dataset is clustered based on coupled metric similarity measure. Secondly, it is proposed to transform the clustering results from benchmark scale to target scale that the two algorithms are named upscaling based on single chain and downscaling based on Lanczos kernel, respectively. Finally, experiments are performed using five public datasets and one real dataset of the Hebei province of China. The results showed that the method can provide us not only competitive performance but also reduce computational cost.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multiscale Clustering Approach for Non‐IID Nominal Data

Chen

Zhao

Tian

2021

Computational Intelligence and Neuroscience

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“…The scale can be equivalent to the following concept: generic concept, level of abstraction or perspective of observation, the same problem, or system can be perceived at different scales based on particular needs [7]. That is called multiscale phenomena and has been widely applied to the academic fields, such as geoscience [8,9] and mathematics [10].…”

Section: Introductionmentioning

confidence: 99%

A Fast Multiscale Clustering Approach Based on DBSCAN

Chen

Zhao

Liang

2021

Wireless Communications and Mobile Computing

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Multiscale brings great benefits for people to observe objects or problems from different perspectives. It has practical significance for clustering on multiscale data. At present, there is a lack of research on the clustering of large-scale data under the premise that clustering results of small-scale datasets have been obtained. If one does cluster on large-scale datasets by using traditional methods, two disadvantages are as follows: (1) Clustering results of small-scale datasets are not utilized. (2) Traditional method will cause more running overhead. Aims at these shortcomings, this paper proposes a multiscale clustering framework based on DBSCAN. This framework uses DBSCAN for clustering small-scale datasets, then introduces algorithm Scaling-Up Cluster Centers (SUCC) generating cluster centers of large-scale datasets by merging clustering results of small-scale datasets, not mining raw large-scale datasets. We show experimentally that, compared to traditional algorithm DBACAN and leading algorithms DBSCAN++ and HDBSCAN, SUCC can provide not only competitive performance but reduce computational cost. In addition, under the guidance of experts, the performance of SUCC is more competitive in accuracy.

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“…Gan et al, [16] proposed an algorithm to mine frequent itemsets from multiple minimum supports. Chen et al, [8] proposed an algorithm to efficiently mine frequent itemsets from small-scale datasets. The main limitation of traditional frequent itemset mining (FIM) concept is that it can be only use to discover itemsets that are absolutely matched in the database.…”

Section: Introductionmentioning

confidence: 99%

Mining Approximate Frequent Itemsets Using Pattern Growth Approach

Bashir

Lai

2021

ITC

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Approximate frequent itemsets (AFI) mining from noisy databases are computationally more expensive than traditional frequent itemset mining. This is because the AFI mining algorithms generate large number of candidate itemsets. This article proposes an algorithm to mine AFIs using pattern growth approach. The major contribution of the proposed approach is it mines core patterns and examines approximate conditions of candidate AFIs directly with single phase and two full scans of database. Related algorithms apply Apriori-based candidate generation and test approach and require multiple phases to obtain complete AFIs. First phase generates core patterns, and second phase examines approximate conditions of core patterns. Specifically, the article proposes novel techniques that how to map transactions on approximate FP-tree, and how to mine AFIs from the conditional patterns of approximate FP-tree. The approximate FP-tree maps transactions on shared branches when the transactions share a similar set of items. This reduces the size of databases and helps to efficiently compute the approximate conditions of candidate itemsets. We compare the performance of our algorithm with the state of the art AFI mining algorithms on benchmark databases. The experiments are analyzed by comparing the processing time of algorithms and scalability of algorithms on varying database size and transaction length. The results show pattern growth approach mines AFIs in less processing time than related Apriori-based algorithms.

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A Fast Approach for Up-Scaling Frequent Itemsets

Cited by 7 publications

References 23 publications

A Multiscale Clustering Approach for Non‐IID Nominal Data

A Multiscale Clustering Approach for Non‐IID Nominal Data

A Fast Multiscale Clustering Approach Based on DBSCAN

Mining Approximate Frequent Itemsets Using Pattern Growth Approach

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