Extracting Frequent (Closed) Seasonal Gradual Patterns Using Closed Itemset Mining

The streams where multiple transactions are associated with the same key are prevalent in practice, e.g., a customer has multiple shopping records arriving at different time. Itemset frequency estimation on such streams is very challenging since sampling based methods, such as the popularly used reservoir sampling, cannot be used. In this article, we propose a novel k -Minimum Value (KMV) synopsis based method to estimate the frequency of itemsets over multi-transaction streams. First, we extract the KMV synopses for each item from the stream. Then, we propose a novel estimator to estimate the frequency of an itemset over the KMV synopses. Comparing to the existing estimator, our method is not only more accurate and efficient to calculate but also follows the downward-closure property. These properties enable the incorporation of our new estimator with existing frequent itemset mining (FIM) algorithm (e.g., FP-Growth) to mine frequent itemsets over multi-transaction streams. To demonstrate this, we implement a KMV synopsis based FIM algorithm by integrating our estimator into existing FIM algorithms, and we prove it is capable of guaranteeing the accuracy of FIM with a bounded size of KMV synopsis. Experimental results on massive streams show our estimator can significantly improve on the accuracy for both estimating itemset frequency and FIM compared to the existing estimators.

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Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases

Mathematics ◽

10.3390/math9040450 ◽

2021 ◽

Vol 9 (4) ◽

pp. 450

Author(s):

Gergely Honti ◽

János Abonyi

Keyword(s):

Climate Change ◽

Extraction Process ◽

Knowledge Extraction ◽

Frequent Itemset ◽

Frequent Itemset Mining ◽

Underlying Structure ◽

Multilayer Network ◽

Interdisciplinary Science ◽

Academic Knowledge ◽

Itemset Mining

Triplestores or resource description framework (RDF) stores are purpose-built databases used to organise, store and share data with context. Knowledge extraction from a large amount of interconnected data requires effective tools and methods to address the complexity and the underlying structure of semantic information. We propose a method that generates an interpretable multilayered network from an RDF database. The method utilises frequent itemset mining (FIM) of the subjects, predicates and the objects of the RDF data, and automatically extracts informative subsets of the database for the analysis. The results are used to form layers in an analysable multidimensional network. The methodology enables a consistent, transparent, multi-aspect-oriented knowledge extraction from the linked dataset. To demonstrate the usability and effectiveness of the methodology, we analyse how the science of sustainability and climate change are structured using the Microsoft Academic Knowledge Graph. In the case study, the FIM forms networks of disciplines to reveal the significant interdisciplinary science communities in sustainability and climate change. The constructed multilayer network then enables an analysis of the significant disciplines and interdisciplinary scientific areas. To demonstrate the proposed knowledge extraction process, we search for interdisciplinary science communities and then measure and rank their multidisciplinary effects. The analysis identifies discipline similarities, pinpointing the similarity between atmospheric science and meteorology as well as between geomorphology and oceanography. The results confirm that frequent itemset mining provides an informative sampled subsets of RDF databases which can be simultaneously analysed as layers of a multilayer network.

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High-utility and diverse itemset mining

Applied Intelligence ◽

10.1007/s10489-020-02063-x ◽

2021 ◽

Author(s):

Amit Verma ◽

Siddharth Dawar ◽

Raman Kumar ◽

Shamkant Navathe ◽

Vikram Goyal

Keyword(s):

Itemset Mining ◽

High Utility

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Dynamic maintenance model for high average-utility pattern mining with deletion operation

Applied Intelligence ◽

10.1007/s10489-021-02539-4 ◽

2021 ◽

Author(s):

Jimmy Ming-Tai Wu ◽

Qian Teng ◽

Shahab Tayeb ◽

Jerry Chun-Wei Lin

Keyword(s):

Pattern Mining ◽

Computational Cost ◽

Practical Applications ◽

Itemset Mining ◽

Dynamic Databases ◽

Speed Up ◽

Dynamic Maintenance ◽

Average Utility ◽

High Utility ◽

Maintenance Model

AbstractThe high average-utility itemset mining (HAUIM) was established to provide a fair measure instead of genetic high-utility itemset mining (HUIM) for revealing the satisfied and interesting patterns. In practical applications, the database is dynamically changed when insertion/deletion operations are performed on databases. Several works were designed to handle the insertion process but fewer studies focused on processing the deletion process for knowledge maintenance. In this paper, we then develop a PRE-HAUI-DEL algorithm that utilizes the pre-large concept on HAUIM for handling transaction deletion in the dynamic databases. The pre-large concept is served as the buffer on HAUIM that reduces the number of database scans while the database is updated particularly in transaction deletion. Two upper-bound values are also established here to reduce the unpromising candidates early which can speed up the computational cost. From the experimental results, the designed PRE-HAUI-DEL algorithm is well performed compared to the Apriori-like model in terms of runtime, memory, and scalability in dynamic databases.

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