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Self learning pattern detection and deviation indication

Siljeholm, Sofia LU and Rönnåker, Johan (2017) In LU-CS-EX 2017-01 EDA920 20162
Department of Computer Science
Abstract
This thesis work produces and evaluates a self learning algorithm that detects user access patterns in a Physical Access Control System (PACS). Self learning algorithms and pattern detection are both well researched areas with numerous methodologies. However, no one method can be used for all problems and this project focuses on hierarchical clustering for a particular problem where no solutions have been documented.

The work was done by analyzing the input data, iteratively implementing and evaluating the clustering algorithm. The evaluation is based on generated data due to lack of real world data. To compensate, a number of scenarios were put together and parameters were extensively tested.

The results show that depending on what... (More)
This thesis work produces and evaluates a self learning algorithm that detects user access patterns in a Physical Access Control System (PACS). Self learning algorithms and pattern detection are both well researched areas with numerous methodologies. However, no one method can be used for all problems and this project focuses on hierarchical clustering for a particular problem where no solutions have been documented.

The work was done by analyzing the input data, iteratively implementing and evaluating the clustering algorithm. The evaluation is based on generated data due to lack of real world data. To compensate, a number of scenarios were put together and parameters were extensively tested.

The results show that depending on what parameters are used the subjective accuracy of the produced hierarchical algorithm will vary greatly. The results also show that the combination of parameters affect how much data the algorithm needs before it can produce useful results.

It can be concluded that hierarchical clustering is applicable on the generated data for a PACS. The variety of parameters and attributes of the input data makes the possibilities of future work based on this project numerous. (Less)
Popular Abstract (Swedish)
I DAGENS KONTORSBYGGNADER TAR MAN SIG OFTAST IN MED PERSONLIGA PASSERKORT OCH PIN-KOD. MED HJÄLP AV MASKININLÄRNING KAN MAN FINNA BAKOMLIGGANDE MÖNSTER OCH UPPTÄCKA OREGELBUNDNA PASSERINGAR SAMT OLOVLIGA INTRÅNG.
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author
Siljeholm, Sofia LU and Rönnåker, Johan
supervisor
organization
course
EDA920 20162
year
type
H3 - Professional qualifications (4 Years - )
subject
keywords
Clustering, Deviation, Machine learning, PACS, Patterns
publication/series
LU-CS-EX 2017-01
report number
LU-CS-EX 2017-01
ISSN
1650-2884
language
English
id
8902813
date added to LUP
2017-02-09 17:31:34
date last changed
2017-02-09 17:31:34
@misc{8902813,
  abstract     = {This thesis work produces and evaluates a self learning algorithm that detects user access patterns in a Physical Access Control System (PACS). Self learning algorithms and pattern detection are both well researched areas with numerous methodologies. However, no one method can be used for all problems and this project focuses on hierarchical clustering for a particular problem where no solutions have been documented.

The work was done by analyzing the input data, iteratively implementing and evaluating the clustering algorithm. The evaluation is based on generated data due to lack of real world data. To compensate, a number of scenarios were put together and parameters were extensively tested. 

The results show that depending on what parameters are used the subjective accuracy of the produced hierarchical algorithm will vary greatly. The results also show that the combination of parameters affect how much data the algorithm needs before it can produce useful results.

It can be concluded that hierarchical clustering is applicable on the generated data for a PACS. The variety of parameters and attributes of the input data makes the possibilities of future work based on this project numerous.},
  author       = {Siljeholm, Sofia and Rönnåker, Johan},
  issn         = {1650-2884},
  keyword      = {Clustering,Deviation,Machine learning,PACS,Patterns},
  language     = {eng},
  note         = {Student Paper},
  series       = {LU-CS-EX 2017-01},
  title        = {Self learning pattern detection and deviation indication},
  year         = {2017},
}