BIB-VERSION:: CS-TR-v2.0
ID:: ncstrl.dartmouthcs//TR2005-550
ENTRY:: July 07, 2005
ORGANIZATION:: Dartmouth College, Computer Science
TITLE:: Mining Frequent and Periodic Association Patterns
TYPE:: Technical Report (paper)
REVISION:: 1
AUTHOR:: Chen, Guanling
AUTHOR:: Huang, Heng
AUTHOR:: Kim, Minkyong
DATE:: July 2005
RETRIEVAL:: For a paper copy, email <reports@cs.dartmouth.edu>
RETRIEVAL:: For a paper copy, write to
       Technical Report Librarian
       Department of Computer Science
       Dartmouth College
       6211 Sudikoff Laboratory
       Hanover, NH 03755-3510
       USA
RETRIEVAL:: PDF at http://www.cs.dartmouth.edu/reports/TR2005-550.pdf
ABSTRACT:: 
 Profiling the clients' movement behaviors is useful for mobility
modeling, anomaly detection, and location prediction.  In this paper,
we study clients' frequent and periodic movement patterns in a campus
wireless network.  We use offline data-mining algorithms to discover
patterns from clients' association history, and analyze the reported
patterns using statistical methods.  Many of our results reflect the
common characteristics of a typical academic campus, though we also
observed some unusual association patterns.  There are two challenges:
one is to remove noise from data for efficient pattern discovery, and
the other is to interpret discovered patterns.  We address the first
challenge using a heuristic-based approach applying domain knowledge.
The second issue is harder to address because we do not have the
knowledge of people's activities, but nonetheless we could make
reasonable interpretation of the common patterns.  
END:: ncstrl.dartmouthcs//TR2005-550