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2Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining
P. Novak, N. Lavrac, and G. Webb. Journal of Machine Learning Research, (2009)
8 years ago by @becker
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set
project:bmbf
citedby:scholar:count:328
emerging
subgroup
inthesis
contrast
mining
pattern
discovery
diss
citedby:scholar:timestamp:2017-4-16
setproject:bmbfcitedby:scholar:count:328emergingsubgroupinthesiscontrastminingpatterndiscoverydisscitedby:scholar:timestamp:2017-4-16
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1Trajectory data mining: A review of methods and applications
J. Mazimpaka, and S. Timpf. Journal of Spatial Information Science, 2016 (13): 61--99 (2016)
8 years ago by @becker
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inthesis
citedby:scholar:timestamp:2017-4-21
mining
citedby:scholar:count:4
sequential
pattern
diss
trajectory
inthesiscitedby:scholar:timestamp:2017-4-21miningcitedby:scholar:count:4sequentialpatterndisstrajectory
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1Trajectory data mining: an overview
Y. Zheng. ACM Transactions on Intelligent Systems and Technology (TIST), 6 (3): 29 (2015)
8 years ago by @becker
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inthesis
citedby:scholar:timestamp:2017-4-21
mining
sequential
pattern
citedby:scholar:count:164
diss
trajectory
inthesiscitedby:scholar:timestamp:2017-4-21miningsequentialpatterncitedby:scholar:count:164disstrajectory
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5Trajectory Pattern Mining
F. Giannotti, M. Nanni, F. Pinelli, and D. Pedreschi. International Conference on Knowledge Discovery and Data Mining (SIGKDD), page 330--339. New York, NY, USA, ACM, (2007)
8 years ago by @becker
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geo
citedby:scholar:timestamp:2017-2-4
inthesis
citedby:scholar:count:816
mining
pattern
diss
spatial
trajectory
geocitedby:scholar:timestamp:2017-2-4inthesiscitedby:scholar:count:816miningpatterndissspatialtrajectory
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2Urban Human Mobility Data Mining: An Overview
K. Zhao, S. Tarkoma, S. Liu, and H. Vo. Big Data, (December 2016)
8 years ago by @becker
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overview
mobility
inthesis
mining
data
urban
survey
diss
overviewmobilityinthesisminingdataurbansurveydiss
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2Using consumer behavior data to reduce energy consumption in smart homes: Applying machine learning to save energy without lowering comfort of inhabitants
D. Schweizer, M. Zehnder, H. Wache, H. Witschel, D. Zanatta, and M. Rodriguez. International Conference on Machine Learning and Applications, page 1123--1129. IEEE, (2015)
8 years ago by @becker
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energy
inthesis
mining
citedby:scholar:timestamp:2017-4-20
sequential
citedby:scholar:count:5
pattern
diss
reduction
energyinthesisminingcitedby:scholar:timestamp:2017-4-20sequentialcitedby:scholar:count:5patterndissreduction
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3Using sequential and non-sequential patterns in predictive web usage mining tasks
B. Mobasher, H. Dai, T. Luo, and M. Nakagawa. International Conference on Data Mining, page 669--672. IEEE, (2002)
8 years ago by @becker
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web
sequentual
access
inthesis
mining
usage
citedby:scholar:timestamp:2017-4-20
pattern
citedby:scholar:count:130
frequent
diss
analysis
websequentualaccessinthesisminingusagecitedby:scholar:timestamp:2017-4-20patterncitedby:scholar:count:130frequentdissanalysis
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1Web access pattern mining--a survey
A. Rajimol, and G. Raju. Data Engineering and Management, Springer, (2012)
8 years ago by @becker
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web
access
inthesis
mining
citedby:scholar:count:4
citedby:scholar:timestamp:2017-4-20
pattern
survey
diss
webaccessinthesisminingcitedby:scholar:count:4citedby:scholar:timestamp:2017-4-20patternsurveydiss
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10Web Mining Research: A Survey
R. Kosala, and H. Blockeel. ACM SIGKDD Explorations Newsletter, 2 (1): 1--15 (June 2000)
8 years ago by @becker
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web
distinction
inthesis
mining
survey
diss
categories
webdistinctioninthesisminingsurveydisscategories
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10Web mining: Information and pattern discovery on the world wide web
R. Cooley, B. Mobasher, and J. Srivastava. International Conference on Tools with Artificial Intelligence (ICTAI), page 558--567. IEEE, IEEE Computer Society, (1997)
8 years ago by @becker
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citedby:scholar:count:1821
web
access
inthesis
mining
citedby:scholar:timestamp:2017-4-20
pattern
diss
citedby:scholar:count:1821webaccessinthesisminingcitedby:scholar:timestamp:2017-4-20patterndiss
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2Web Usage Mining
B. Liu, B. Mobasher, and O. Nasraoui. page 527--603. Springer Berlin Heidelberg, Berlin, Heidelberg, (2011)
7 years ago by @becker
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web
inthesis
mining
usage
diss
webinthesisminingusagediss
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6Web usage mining as a tool for personalization: A survey
D. Pierrakos, G. Paliouras, C. Papatheodorou, and C. Spyropoulos. User modeling and user-adapted interaction, 13 (4): 311--372 (2003)
7 years ago by @becker
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citedby:scholar:timestamp:2017-7-17
web
inthesis
mining
personalization
usage
review
survey
citedby:scholar:count:493
diss
model
citedby:scholar:timestamp:2017-7-17webinthesisminingpersonalizationusagereviewsurveycitedby:scholar:count:493dissmodel
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5Web usage mining: Discovery and applications of usage patterns from web data
J. Srivastava, R. Cooley, M. Deshpande, and P. Tan. ACM SIGKDD Explorations Newsletter, 1 (2): 12--23 (2000)
8 years ago by @becker
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log
web
access
inthesis
mining
usage
citedby:scholar:timestamp:2017-4-20
pattern
citedby:scholar:count:2829
diss
logwebaccessinthesisminingusagecitedby:scholar:timestamp:2017-4-20patterncitedby:scholar:count:2829diss
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2webspade: A parallel sequence mining algorithm to analyze web log data
A. Demiriz. International Conference on Data Mining (ICDM), page 755--758. IEEE, (2002)
8 years ago by @becker
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web
access
inthesis
citedby:scholar:count:23
mining
citedby:scholar:timestamp:2017-4-20
sequential
pattern
diss
algorithm
webaccessinthesiscitedby:scholar:count:23miningcitedby:scholar:timestamp:2017-4-20sequentialpatterndissalgorithm
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3WhereNext: A location predictor on trajectory pattern mining
A. Monreale, F. Pinelli, R. Trasarti, and F. Giannotti. International Conference on Knowledge Discovery and Data Mining, page 637--646. New York, NY, USA, ACM, (2009)
8 years ago by @becker
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geo
predicition
citedby:scholar:timestamp:2017-2-4
inthesis
mining
pattern
diss
location
spatial
trajectory
citedby:scholar:count:355
geopredicitioncitedby:scholar:timestamp:2017-2-4inthesisminingpatterndisslocationspatialtrajectorycitedby:scholar:count:355
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publications (hide)75
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2Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining

1Trajectory data mining: A review of methods and applications

1Trajectory data mining: an overview

5Trajectory Pattern Mining

2Urban Human Mobility Data Mining: An Overview

2Using consumer behavior data to reduce energy consumption in smart homes: Applying machine learning to save energy without lowering comfort of inhabitants

3Using sequential and non-sequential patterns in predictive web usage mining tasks

1Web access pattern mining--a survey

10Web Mining Research: A Survey

10Web mining: Information and pattern discovery on the world wide web

2Web Usage Mining

6Web usage mining as a tool for personalization: A survey

5Web usage mining: Discovery and applications of usage patterns from web data

2webspade: A parallel sequence mining algorithm to analyze web log data

3WhereNext: A location predictor on trajectory pattern mining

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