Unsupervised Rare Pattern Mining

Koh, Yun Sing; Ravana, Sri Devi

doi:10.1145/2898359

Cited by 48 publications

(25 citation statements)

References 35 publications

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“…SPMF has the largest collection of implementations of various algorithms for pattern mining algorithms (i.e., FPM, ARM, SPM, etc.) and provides a userfriendly graphical interface 3 . In particular, it also provides the relevant materials, including source codes, documentation, user instruction, benchmark datasets, data generators, and academic papers.…”

Section: Open-source Softwarementioning

confidence: 99%

A Survey of Utility-Oriented Pattern Mining

Gan

Lin

Fournier‐Viger

et al. 2021

IEEE Trans. Knowl. Data Eng.

240

148

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The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and evaluating the usefulness of different kinds of patterns, many techniques/constraints have been proposed, such as support, confidence, sequence order, and utility parameters (e.g., weight, price, profit, quantity, etc.). In recent years, there has been an increasing demand for utility-oriented pattern mining (UPM). UPM is a vital task, with numerous high-impact applications, including cross-marketing, e-commerce, finance, medical, and biomedical applications. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of UPM. First, we introduce an in-depth understanding of UPM, including concepts, examples, and comparisons with related concepts. A taxonomy of the most common and state-of-the-art approaches for mining different kinds of high-utility patterns is presented, including Apriori-based, tree-based, projection-based, vertical-/horizontal-data-format-based, and other hybrid approaches. A comprehensive review of advanced topics of existing high-utility pattern mining techniques is offered, with a discussion of their pros and cons. Finally, we present several well-known open-source software packages for UPM. We conclude our survey with a discussion on open and practical challenges in this field.

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Section: Open-source Softwarementioning

confidence: 99%

A Survey of Utility-Oriented Pattern Mining

Gan

Lin

Fournier‐Viger

et al. 2021

IEEE Trans. Knowl. Data Eng.

240

148

View full text Add to dashboard Cite

show abstract

“…Frequent itemset mining is useful in many applications with varied range of domains like image classification [7], network traffic analysis [8], Bioinformatics [9], detection of malware [10], analyzing the typical behavior of customers [11] and elearning [12]. Frequent item set mining is also helpful to discover, connected patterns [13,14], designs in sequences [15,16] and graphs [17], uncommon patterns [18].…”

Section: Frequent Pattern Miningmentioning

confidence: 99%

A Comprehensive Survey of Pattern Mining: Challenges and Opportunities

Upadhyay¹,

Pandey²,

Kohli³

2018

IJCA

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Pattern mining is an important field of data mining. The fundamental task of data mining is to explore the database to find out sequential, frequent patterns. In recent years, data mining has shifted its focus to design methods for discovering patterns with user expectations. In this regard various types of pattern mining methods have been proposed. Frequent pattern mining, sequential pattern mining, temporal pattern mining, and constraint based pattern mining. Pattern mining has various useful real-life applications such as market basket analysis, e-learning, social network analysis, web page, click sequences, Bioinformatics, etc., this paper presents a survey of various types of pattern mining. The main goal of this paper is to present both an introduction to all pattern mining and a survey of various algorithms, challenges and research opportunities. This paper not only discusses the problems of pattern mining and its related applications, but also the extensions and possible future improvements in this field.

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“…The last topic we consider in this section is rare event detection [125]–[128]. Rare events are events that occur very infrequently.…”

Section: Detecting and Assessing Threatsmentioning

confidence: 99%

Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

Dahmen

Cook

Wang

et al. 2017

J Reliable Intell Environ

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Smart home design has undergone a metamorphosis in recent years. The field has evolved from designing theoretical smart home frameworks and performing scripted tasks in laboratories. Instead, we now find robust smart home technologies that are commonly used by large segments of the population in a variety of settings. Recent smart home applications are focused on activity recognition, health monitoring, and automation. In this paper, we take a look at another important role for smart homes: security. We first explore the numerous ways smart homes can and do provide protection for their residents. Next, we provide a comparative analysis of the alternative tools and research that has been developed for this purpose. We investigate not only existing commercial products that have been introduced but also discuss the numerous research that has been focused on detecting and identifying potential threats. Finally, we close with open challenges and ideas for future research that will keep individuals secure and healthy while in their own homes.

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Unsupervised Rare Pattern Mining

Cited by 48 publications

References 35 publications

A Survey of Utility-Oriented Pattern Mining

A Survey of Utility-Oriented Pattern Mining

A Comprehensive Survey of Pattern Mining: Challenges and Opportunities

Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

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