# Risk and Pattern Analysis of Pakistani Crime Data Using Unsupervised Learning Techniques
## 6 Conclusions and Future Work  
Data mining techniques and tools have brought remarkable changes to the way data are analysed as well as revealing useful information. The main aim of this study is to provide a useful analysis that can lead to the development of efficient solutions to help concerned police teams and analysts solve crime cases by analysing similar criminal activities and crime patterns. For this matter, crime data from different police stations in Lahore, Pakistan, are collected and thoroughly analysed. Primarily, the data were available in the hard format written in the Urdu language. Data pre-processing was applied to the gathered crime data for its conversion into structured tabular form. To extract trends present in the dataset, data visualization analysis was performed which helped in effective exploratory analysis. Data mining techniques, namely cluster analysis and association rule mining, were applied for automated risk identification of criminal activities that are expected to happen at a particular location, time, and day. K-modes clustering algorithm is applied to group similar criminal activities. Apriori algorithm is used for the extraction of the frequent crime patterns and associations among crime type, day type, time of day, and crime location. This work can be an important step forward to reduce the growing rate  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\#** & **Strong Association Rules (in Descending Order of Lift** & **S** & **C** & **L** \\  & **Values**) & & & \\ \hline
**1** & “[DayType = Weekend, Distance = 3 km] \(\Rightarrow\) [CrimeType = & 9 & 1 & 75.429 \\
**2** & “[DayTime = Morning, DayType = Weekend, Direction =  south] \(\Rightarrow\) [CrimeType = Marriage ordinance]” & 9 & 1 & 69.123 \\
**3** & “[DayType = Weekdays, Direction = SOUTH EAST, Distance = 3 km] \(\Rightarrow\) [CrimeType = Conspiracy]” & 8 & 1 & 68.67 \\
**4** & “[DayTime = Morning, DayType = Weekend, Direction =  North, Distance = 2 km] \(\Rightarrow\) [CrimeType = Kidnapping]” & 8 & 1 & 67.12 \\
**5** & “[DayTime = After-noon, DayType = Weekdays, Direction =  EAST, Distance = 1 km] \(\Rightarrow\) [CrimeType = Robberly]” & 8 & 1 & 60.28 \\
**6** & “[DayTime = After-Noon, DayType = Weekdays, Direction =  SOUTH, Distance = 3 km] \(\Rightarrow\) [CrimeType = Fraud]” & 7 & 1 & 59.1 \\
**7** & “[DayTime = Night, DayType = Weekend, Direction = WEST, Distance = 1 km] \(\Rightarrow\) [CrimeType = Theft]” & 7 & 1 & 53.12 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Sample of strong and interesting association rules mined from the crime dataset.  
of crime in the world. The output of this research is very helpful for the concerned police officers and analysts in cracking and solving crime cases with less effort.  
In the future, the dimensionality of the dataset needs expansion for analysing the crime patterns involved in different cities of Pakistan based on hotspots. The deep learning approaches could be developed and identify risky locations of a real-time crime at particular region from an image. With the help of solved crime case records, the crime pattern of a particular criminal can also be identified which leads to the identification of criminal network groups. Furthermore, machine learning techniques like classification and regression analysis can be applied to get useful trends from the data.  
Author Contributions: Conceptualization, F.F., Methodology, M.T.H., Software, S.M., Validation, H.A., Formal Analysis, M.I., Investigation, Resources, M.A., Data curation, A.M., Writ-ing-Original, M.T.H., Original Draft Preparation, F.F., Review and Editing, M.I., Supervision, E.-A.-A. All authors have read and agreed to the published version of manuscript.  
This research received no external funding.  
Not Applicable.  
The authors have no conflict of interest.