Criminal investigation plays an important role in
law enforcement agencies while analyzing crimes. This can
help in finding suspects and for better attribution of past
crimes. So the use of pattern based approaches will have the
potential to assist crime analyze experts in discovering new
patterns of criminal activities. So the research is extended in
this area in finding new crime patterns. Since frequent
pattern mining problem has been introduced, researchers
have been developed many ways and also extended to
different useful emerging patterns such as closed, maximal,
cyclic, periodic, and popular patterns. In this paper, we
introduced popular crime patterns which give the popularity
of each crime or incident among the entire crime database.
The process used for mining popular crime patterns is
discussed as PCrime-growth algorithm. The compact data
structure called PCrime-tree is implemented in this
algorithm. Finally our experiment results have been shown
which gives information of compact, space efficient and time
efficient of our proposed algorithm.
B.V.S.Varma : is Associate Professor in the department of Computer
Science & Engineering at Ideal Institute of Technology, India. He
obtained M.Tech degree in CSE from Dr. M.G.R.University and
B.Tech in CSE from Madras University. He has 13 years of Teaching
Experience He research interest includes Data Mining.
V.Valli Kumari : is professor in the department of Computer Science
& Systems Engineering and Director of Computer Center at Andhra
University, India. She has received best Researcher Award from the
same university. She has 17 years of teaching experience and guided
several Ph.Ds. she is an active researcher and executed several
research projects. She has published more than 120 research papers
in international conferences and journals, authored several book
chapters and chaired several international conferences.
Crime Patterns
Popular Patterns
Crime
databases
PCrime-growth
In this paper we introduced a new algorithm called
PCrime-growth algorithm that finds popular crime
patterns from Crime datasets. With the first database scan
it constructs PCrime-tree and also computes support of a
crime, maximum crime transaction length and popularity
of each crime. After that the algorithm performs for
super-pattern popularity for unpopular crimes to prune the
crime database. After second scan its going to compute
length of each crime transaction by eliminates unpopular
crimes and will extracts popular crime patterns from the
crime databases. Our experimental results showed that
our PCrime-tree is time and space efficient for both sparse
and dense datasets. In addition these results are also
affected on crime datasets on construction of PCrime-tree
and mining of popular crime patterns to be time efficient.
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