Nonoccurring Behavior Analytics: A New Area

2015/11/01 by Longbing Cao, Philip S. Yu, Vipin Kumar · 3 citations
Computer Science · #Data Stream Mining Techniques #Software Engineering Research #Advanced Malware Detection Techniques

paper · doi:10.1109/mis.2015.105

openalex publication_date 2015/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/11

Abstract

Nonoccurring behaviors (NOBs) refer to those behaviors that should happen but do not take place for some reasons. They are widely seen in online, business, government, health, medical, scientific, and social data and applications. Very limited research has been undertaken to explore such behaviors owing to their invisibility and the significant challenges in analyzing NOBs. This article outlines the concept, intrinsic characteristics, significant challenges, main issues, research directions, and state-of-the-art work related to NOBs, followed by the prospects for and applications of NOB analytics.

Citations

• SPADE: An Efficient Algorithm for Mining Frequent Sequences
• Non-IIDness Learning in Behavioral and Social Data

Cited by

• Data Science: Challenges and Directions
• Semantics of negative sequential patterns
• Longbing Cao [wikipedia]

Related

• In-Depth Behavior Understanding and Use: The Behavior Informatics Approach
• Non-IIDness Learning in Behavioral and Social Data
• Measuring Behaviour
• Behavioral Aspects of Social Network Analysis
• The structure of behavioral data