NDLI: Unsupervised Rare Pattern Mining: A Survey

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ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 11

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 10

Issue 4(Special Issue on SIGKDD 2014, Special Issue on BIGCHAT and Regular Papers), July 2016

Introduction to the Special Issue of Best Papers in ACM SIGKDD 2014

Product Selection Problem: Improve Market Share by Learning Consumer Behavior

Catching Synchronized Behaviors in Large Networks: A Graph Mining Approach

Heterogeneous Translated Hashing: A Scalable Solution Towards Multi-Modal Similarity Search

Guest Editorial: Special Issue on Connected Health at Big Data Era (BigChat): A TKDD Special Issue

Kernelized Information-Theoretic Metric Learning for Cancer Diagnosis Using High-Dimensional Molecular Profiling Data

Jointly Modeling Label and Feature Heterogeneity in Medical Informatics

Mining Dual Networks: Models, Algorithms, and Applications

Biomedical Ontology Quality Assurance Using a Big Data Approach

Less is More: Building Selective Anomaly Ensembles

Co-Clustering Structural Temporal Data with Applications to Semiconductor Manufacturing

Inferring Dynamic Diffusion Networks in Online Media

Unsupervised Rare Pattern Mining: A Survey

CGC: A Flexible and Robust Approach to Integrating Co-Regularized Multi-Domain Graph for Clustering

Spatial-Proximity Optimization for Rapid Task Group Deployment

Featuring, Detecting, and Visualizing Human Sentiment in Chinese Micro-Blog

Eigen-Optimization on Large Graphs by Edge Manipulation

Issue 3, February 2016

Issue 2, October 2015

Issue 1, July 2015

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 9

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 8

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 7

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 6

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 5

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 4

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 3

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 2

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 1

Unsupervised Rare Pattern Mining: A Survey

Content Provider	ACM Digital Library
Author	Ravana, Sri Devi Koh, Yun Sing
Copyright Year	2016
Abstract	Association rule mining was first introduced to examine patterns among frequent items. The original motivation for seeking these rules arose from need to examine customer purchasing behaviour in supermarket transaction data. It seeks to identify combinations of items or itemsets, whose presence in a transaction affects the likelihood of the presence of another specific item or itemsets. In recent years, there has been an increasing demand for rare association rule mining. Detecting rare patterns in data is a vital task, with numerous high-impact applications including medical, finance, and security. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for rare pattern mining. We investigate the problems in finding rare rules using traditional association rule mining. As rare association rule mining has not been well explored, there is still specific groundwork that needs to be established. We will discuss some of the major issues in rare association rule mining and also look at current algorithms. As a contribution, we give a general framework for categorizing algorithms: Apriori and Tree based. We highlight the differences between these methods. Finally, we present several real-world application using rare pattern mining in diverse domains. We conclude our survey with a discussion on open and practical challenges in the field.
Starting Page	1
Ending Page	29
Page Count	29
File Format	PDF
ISSN	15564681
e-ISSN	1556472X
DOI	10.1145/2898359
Volume Number	10
Issue Number	4
Journal	ACM Transactions on Knowledge Discovery from Data (TKDD)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-05-24
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Association rule mining Infrequent patterns Rare rules
Content Type	Text
Resource Type	Article
Subject	Computer Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Mining and representing rare association rules through the use of genetic programming

Confabulation-Inspired Association Rule Mining for Rare and Frequent Itemsets

Research on Mining Sequential Positive and Negative Association Rules

On characterization and discovery of minimal unexpected patterns in rule discovery

Formal and computational properties of the confidence boost of association rules

A taxonomy of sequential pattern mining algorithms

Discovering Transitional Patterns and Their Significant Milestones in Transaction Databases

Infrequent Weighted Itemset Mining Using Frequent Pattern Growth

Association rules optimization: a survey.

Unsupervised Rare Pattern Mining: A Survey

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