NDLI: Discovering frequent behaviors: time is an essential element of the context

Content Provider	Springer Nature Link
Author	Saleh, Bashar Masseglia, Florent
Copyright Year	2010
Abstract	One of the most popular problems in usage mining is the discovery of frequent behaviors. It relies on the extraction of frequent itemsets from usage databases. However, those databases are usually considered as a whole, and therefore, itemsets are extracted over the entire set of records. Our claim is that possible subsets, hidden within the structure of the data and containing relevant itemsets, may exist. These subsets, as well as the itemsets they contain, depend on the context. Time is an essential element of the context. The users’ intents will differ from one period to another. Behaviors over Christmas will be different from those extracted during the summer. Unfortunately, these periods might be lost because of arbitrary divisions of the data. The goal of our work is to find itemsets that are frequent over a specific period, but would not be extracted by traditional methods since their support is very low over the whole dataset. We introduce the definition of solid itemsets, which represent coherent and compact behaviors over specific periods, and we propose Sim, an algorithm for their extraction.
Starting Page	311
Ending Page	331
Page Count	21
File Format	PDF
ISSN	02191377
Journal	Knowledge and Information Systems
Volume Number	28
Issue Number	2
e-ISSN	02193116
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-11-20
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Itemsets Periods Time-aware Business Information Systems Information Systems and Communication Service
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Information Systems Human-Computer Interaction Hardware and Architecture Software

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

Discovering Frequent Behaviors: Time is an Essential Element of the Context

On condensed representations of constrained frequent patterns

Summarization – compressing data into an informative representation

Top-K Miner: top-K identical frequent itemsets discovery without user support threshold

Performance study of distributed Apriori-like frequent itemsets mining

A survey on algorithms for mining frequent itemsets over data streams

DSM-FI: an efficient algorithm for mining frequent itemsets in data streams

PGLCM: efficient parallel mining of closed frequent gradual itemsets

Efficient mining of maximal frequent itemsets from databases on a cluster of workstations

Discovering frequent behaviors: time is an essential element of the context

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Discovering Frequent Behaviors: Time is an Essential Element of the Context

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Discovering frequent behaviors: time is an essential element of the context