A crime is an act which is against the laws of a country or region. The technique which is used to find areas on a map which have high crime intensity is known as crime hotspot prediction. The technique uses the crime data which includes the area with crime rate and predict the future location with high crime intensity. The motivation of crime hotspot prediction is to raise people’s awareness regarding the dangerous location in certain time period. It can help for police resource allocation for creating a safe environment. The paper presents survey of different types of data mining techniques for crime hotspots prediction.
Crime Data Analysis, Visualization and Prediction using Data MiningAnavadya Shibu
This paper presents a general idea about the
model of Data Mining techniques and diverse crimes. It also
provides an inclusive survey of competent and valuable
techniques on data mining for crime data analysis. The
objective of the data mining is to recognize patterns in
criminal manners in order to predict crime anticipate
criminal activity and prevent it. This project implements a
novel data mining techniques like KNN, Text Clustering, IR
tree for investigating the crime data sets and sorts out the
accessible problems. The collective knowledge of various
data mining algorithms tend certainly to afford an
enhanced, incorporated, and precise result over the crime
prediction in the banking sectors Our law enforcement
organizations require to be adequately outfitted to defeat
and prevent the crime. This project is developed using Java
as front-end and MySQL as back-end. Supporting
applications like Sunset, NetBeans are used to make the
portal more interactive.
Data mining and machine learning have become a vital part of crime detection and prevention. In this
research, we use WEKA, an open source data mining software, to conduct a comparative study between the
violent crime patterns from the Communities and Crime Unnormalized Dataset provided by the University
of California-Irvine repository and actual crime statistical data for the state of Mississippi that has been
provided by neighborhoodscout.com. We implemented the Linear Regression, Additive Regression, and
Decision Stump algorithms using the same finite set of features, on the Communities and Crime Dataset.
Overall, the linear regression algorithm performed the best among the three selected algorithms. The scope
of this project is to prove how effective and accurate the machine learning algorithms used in data mining
analysis can be at predicting violent crime patterns.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Viterbi optimization for crime detection and identificationTELKOMNIKA JOURNAL
In this paper, we introduce two types of hybridization. The first contribution is the hybridization between the Viterbi algorithm and Baum Welch in order to predict crime locations. While the second contribution considers the optimization based on decision tree (DT) in combination with the Viterbi algorithm for criminal identification using Iraq and India crime dataset. This work is based on our previous work [1]. The main goal is to enhance the results of the model in both consuming times and to get a more accurate model. The obtained results proved the achievement of both goals in an efficient way.
Crime Data Analysis, Visualization and Prediction using Data MiningAnavadya Shibu
This paper presents a general idea about the
model of Data Mining techniques and diverse crimes. It also
provides an inclusive survey of competent and valuable
techniques on data mining for crime data analysis. The
objective of the data mining is to recognize patterns in
criminal manners in order to predict crime anticipate
criminal activity and prevent it. This project implements a
novel data mining techniques like KNN, Text Clustering, IR
tree for investigating the crime data sets and sorts out the
accessible problems. The collective knowledge of various
data mining algorithms tend certainly to afford an
enhanced, incorporated, and precise result over the crime
prediction in the banking sectors Our law enforcement
organizations require to be adequately outfitted to defeat
and prevent the crime. This project is developed using Java
as front-end and MySQL as back-end. Supporting
applications like Sunset, NetBeans are used to make the
portal more interactive.
Data mining and machine learning have become a vital part of crime detection and prevention. In this
research, we use WEKA, an open source data mining software, to conduct a comparative study between the
violent crime patterns from the Communities and Crime Unnormalized Dataset provided by the University
of California-Irvine repository and actual crime statistical data for the state of Mississippi that has been
provided by neighborhoodscout.com. We implemented the Linear Regression, Additive Regression, and
Decision Stump algorithms using the same finite set of features, on the Communities and Crime Dataset.
Overall, the linear regression algorithm performed the best among the three selected algorithms. The scope
of this project is to prove how effective and accurate the machine learning algorithms used in data mining
analysis can be at predicting violent crime patterns.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Viterbi optimization for crime detection and identificationTELKOMNIKA JOURNAL
In this paper, we introduce two types of hybridization. The first contribution is the hybridization between the Viterbi algorithm and Baum Welch in order to predict crime locations. While the second contribution considers the optimization based on decision tree (DT) in combination with the Viterbi algorithm for criminal identification using Iraq and India crime dataset. This work is based on our previous work [1]. The main goal is to enhance the results of the model in both consuming times and to get a more accurate model. The obtained results proved the achievement of both goals in an efficient way.
Survey of Data Mining Techniques on Crime Data Analysisijdmtaiir
-Data mining is a process of extracting knowledge
from huge amount of data stored in databases, data warehouses
and data repositories. Crime is an interesting application where
data mining plays an important role in terms of prediction and
analysis. Clustering is the process of combining data objects
into groups. The data objects within the group are very similar
and very dissimilar as well when compared to objects of other
groups. This paper presents detailed study on clustering
techniques and its role on crime applications. This study also
helps crime branch for better prediction and classification of
crimes.
PREDICTIVE MODELLING OF CRIME DATASET USING DATA MININGIJDKP
With a substantial increase in crime across the globe, there is a need for analysing the crime data to lower
the crime rate. This helps the police and citizens to take necessary actions and solve the crimes faster. In
this paper, data mining techniques are applied to crime data for predicting features that affect the high
crime rate. Supervised learning uses data sets to train, test and get desired results on them whereas
Unsupervised learning divides an inconsistent, unstructured data into classes or clusters. Decision trees,
Naïve Bayes and Regression are some of the supervised learning methods in data mining and machine
learning on previously collected data and thus used for predicting the features responsible for causing
crime in a region or locality. Based on the rankings of the features, the Crimes Record Bureau and Police
Department can take necessary actions to decrease the probability of occurrence of the crime.
PREDICTIVE MODELLING OF CRIME DATASET USING DATA MININGIJDKP
With a substantial increase in crime across the globe, there is a need for analysing the crime data to lower the crime rate. This helps the police and citizens to take necessary actions and solve the crimes faster. In this paper, data mining techniques are applied to crime data for predicting features that affect the high crime rate. Supervised learning uses data sets to train, test and get desired results on them whereas Unsupervised learning divides an inconsistent, unstructured data into classes or clusters. Decision trees, Naïve Bayes and Regression are some of the supervised learning methods in data mining and machine learning on previously collected data and thus used for predicting the features responsible for causing crime in a region or locality. Based on the rankings of the features, the Crimes Record Bureau and Police Department can take necessary actions to decrease the probability of occurrence of the crime.
Since its debut in 2010, Apache Spark has become one of the most popular Big Data technologies in the Apache open source ecosystem. In addition to enabling processing of large data sets through its distributed computing architecture, Spark provides out-of-the-box support for machine learning, streaming and graph processing in a single framework. Spark has been supported by companies like Microsoft, Google, Amazon and IBM and in financial services, companies like Blackrock (http://bit.ly/1Q1DVJH ) and Bloomberg (http://bit.ly/29LXbPv ) have started to integrate Apache Spark into their tool chain and the interest is growing. Unlike other big-data technologies which require intensive programming using Java etc., Spark enables data scientists to work with a big-data technology using higher level languages like Python and R making it accessible to conduct experiments and for rapid prototyping.
In this talk, we will introduce Apache Spark and discuss the key features that differentiate Apache Spark from other technologies. We will provide examples on how Apache Spark can help scale analytics and discuss how the machine learning API could be used to solve large-scale machine learning problems using Spark’s distributed computing framework. We will also illustrate enterprise use cases for scaling analytics with Apache Spark.
Hybrid Approach for Intrusion Detection Model Using Combination of K-Means Cl...theijes
Any violation of information security policy with malicious intent is regarded as an intrusion. The fast evolving new kind of intrusions poses a very serious threat to system security, although there has been the rapid development of several security tools to counter the growing threats, intrusive activities are still growing. Many Intrusion Detection models have been implemented since the concept of Intrusion Detection emerged, but the majority of the existing Intrusion detection models have many drawbacks which include but not limited to low accuracy in detection, high false alarm rates, adaptability weakness, inability to detect new intrusions etc. The main aim of this study is proposing a model that combined simple K-Means clustering Algorithms and Random Forest classification technique that will have minimum false alarms rate and high accuracy detection rate. The experiment was carried out in WEKA 3.8 using the NSL-KDD dataset to process the dataset and obtained the results. At the end of training and testing of the proposed study, the results indicated that the proposed approach achieved improved accuracy and reduced false alarm rates by 99.98% and 0.14% respectively.
Statistical Prediction for analyzing Epidemiological Characteristics of COVID...Nuwan Sriyantha Bandara
In this presentation, we propose a novel integrated model for analyzing the characteristics of the epidemiological curve of COVID-19 by utilizing an enhanced compartmental statistical prediction model which is developed conferring susceptible-infectious susceptible (SIS) model, susceptible-infectious-removed (SIR) model, Dirichlet process model, and the interpretive structural model.
The oral presentation of the research has been presented at the International Research Conference 2020 of Sri Lanka Technological Campus on 17th June, 2020.
Abstract Link: http://repo.sltc.ac.lk/handle/1/82
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
According to the Nilson report, the global Credit card and debit card fraud resulted in losses amounting to $24.71 billion in 2016 and 72% were bored by the Card issuers. Therefore, the card issue companies are eager to predict the fraud in real time and in advance to reduce their loss and protect their revenue. The goal of the project is to provide fraud analytics for credit card issue companies to predict fraud in real-time and in advance. By building a supervised fraud prediction model, we are aiming to capture the maximum number of real frauds while limiting the occurrence of mis-flagged frauds, in order to achieve a win-win situation both maximize our ROI and achieve customer satisfaction.
An intrusion detection model based on fuzzy membership function using gnpeSAT Journals
Abstract As the Internet facilities increasing over the world, threats, attacks or intrusions over the Internet are also increasing. Therefore, an intrusion detection model is required to detect intrusion that going to threaten CIA of internet resources. A GNP based fuzzy membership function is much more suitable for identifying such kind of intrusions. A GNP which is a combination of GA and GP applied to extract association rules. A combined GNP-fuzzy membership method would help us to extract important association rules from DARPA 98/99 dataset rather than all rules from DARPA 98/99 dataset. Then the extracted association rules would be updated using genetic operations and also stored into rule pool. In classification, association rules will be classified as normal or intrusion based on calculated match degree. The classified association rules will be stored separately in two different rule pools. Normal rules in normal rule pool and intrusion rules in intrusion rule pool. For the new data match degree will be calculated based on available normal rules and intrusion rules. Then this calculated match degree will help us to identify whether the new data normal or intrusion. Keywords: Fuzzy membership function, Genetic network programming, Genetic algorithm, DARPA 98/99 dataset and Intrusion detection.
Data mining is an integrated field, depicted technologies in combination to the areas having database, learning by machine, statistical study, and recognition in patterns of same type, information regeneration, A.I networks, knowledge-based portfolios, artificial intelligence, neural network, and data determination. In real terms, mining of data is the investigation of provisional data sets for finding hidden connections and to gather the information in peculiar form which are justifiable and understandable to the owner of gather or mined data. An unsupervised formula which differentiate data components into collections by which the components in similar group are more allied to one other and items in rest of cluster seems to be non-allied, by the criteria of measurement of equality or predictability is called process of clustering. Cluster analysis is a relegating task that is utilized to identify same group of object and it is additionally one of the most widely used method for many practical application in data mining. It is a method of grouping objects, where objects can be physical, such as a student or may be a summary such as customer comportment, handwriting. It has been proposed many clustering algorithms that it falls into the different clustering methods. The intention of this paper is to provide a relegation of some prominent clustering algorithms.
A predictive model for mapping crime using big data analyticseSAT Journals
Abstract Crime reduction and prevention challenges in today’s world are becoming increasingly complex and are in need of a new technique that can handle the vast amount of information that is being generated. Traditional police capabilities mostly fall short in depicting the original division of criminal activities, thus contribute less in the suitable allocation of police services. In this paper methods are described for crime event forecasting, using Hadoop, by studying the geographical areas which are at greater risk and outside the traditional policing limits. The developed method makes the use of a geographical crime mapping algorithm to identify areas that have relatively high cases of crime. The term used for such places is hot spots. The identified hotspot clusters give valuable data that can be used to train the artificial neural network which further can model the trends of crime. The artificial neural network specification and estimation approach is enhanced by processing capability of Hadoop platform. Keywords— Crime forecasting; Cluster analysis; artificial neural networks; Patrolling; Big data; Hadoop; Gamma test.
Survey of Data Mining Techniques on Crime Data Analysisijdmtaiir
-Data mining is a process of extracting knowledge
from huge amount of data stored in databases, data warehouses
and data repositories. Crime is an interesting application where
data mining plays an important role in terms of prediction and
analysis. Clustering is the process of combining data objects
into groups. The data objects within the group are very similar
and very dissimilar as well when compared to objects of other
groups. This paper presents detailed study on clustering
techniques and its role on crime applications. This study also
helps crime branch for better prediction and classification of
crimes.
PREDICTIVE MODELLING OF CRIME DATASET USING DATA MININGIJDKP
With a substantial increase in crime across the globe, there is a need for analysing the crime data to lower
the crime rate. This helps the police and citizens to take necessary actions and solve the crimes faster. In
this paper, data mining techniques are applied to crime data for predicting features that affect the high
crime rate. Supervised learning uses data sets to train, test and get desired results on them whereas
Unsupervised learning divides an inconsistent, unstructured data into classes or clusters. Decision trees,
Naïve Bayes and Regression are some of the supervised learning methods in data mining and machine
learning on previously collected data and thus used for predicting the features responsible for causing
crime in a region or locality. Based on the rankings of the features, the Crimes Record Bureau and Police
Department can take necessary actions to decrease the probability of occurrence of the crime.
PREDICTIVE MODELLING OF CRIME DATASET USING DATA MININGIJDKP
With a substantial increase in crime across the globe, there is a need for analysing the crime data to lower the crime rate. This helps the police and citizens to take necessary actions and solve the crimes faster. In this paper, data mining techniques are applied to crime data for predicting features that affect the high crime rate. Supervised learning uses data sets to train, test and get desired results on them whereas Unsupervised learning divides an inconsistent, unstructured data into classes or clusters. Decision trees, Naïve Bayes and Regression are some of the supervised learning methods in data mining and machine learning on previously collected data and thus used for predicting the features responsible for causing crime in a region or locality. Based on the rankings of the features, the Crimes Record Bureau and Police Department can take necessary actions to decrease the probability of occurrence of the crime.
Since its debut in 2010, Apache Spark has become one of the most popular Big Data technologies in the Apache open source ecosystem. In addition to enabling processing of large data sets through its distributed computing architecture, Spark provides out-of-the-box support for machine learning, streaming and graph processing in a single framework. Spark has been supported by companies like Microsoft, Google, Amazon and IBM and in financial services, companies like Blackrock (http://bit.ly/1Q1DVJH ) and Bloomberg (http://bit.ly/29LXbPv ) have started to integrate Apache Spark into their tool chain and the interest is growing. Unlike other big-data technologies which require intensive programming using Java etc., Spark enables data scientists to work with a big-data technology using higher level languages like Python and R making it accessible to conduct experiments and for rapid prototyping.
In this talk, we will introduce Apache Spark and discuss the key features that differentiate Apache Spark from other technologies. We will provide examples on how Apache Spark can help scale analytics and discuss how the machine learning API could be used to solve large-scale machine learning problems using Spark’s distributed computing framework. We will also illustrate enterprise use cases for scaling analytics with Apache Spark.
Hybrid Approach for Intrusion Detection Model Using Combination of K-Means Cl...theijes
Any violation of information security policy with malicious intent is regarded as an intrusion. The fast evolving new kind of intrusions poses a very serious threat to system security, although there has been the rapid development of several security tools to counter the growing threats, intrusive activities are still growing. Many Intrusion Detection models have been implemented since the concept of Intrusion Detection emerged, but the majority of the existing Intrusion detection models have many drawbacks which include but not limited to low accuracy in detection, high false alarm rates, adaptability weakness, inability to detect new intrusions etc. The main aim of this study is proposing a model that combined simple K-Means clustering Algorithms and Random Forest classification technique that will have minimum false alarms rate and high accuracy detection rate. The experiment was carried out in WEKA 3.8 using the NSL-KDD dataset to process the dataset and obtained the results. At the end of training and testing of the proposed study, the results indicated that the proposed approach achieved improved accuracy and reduced false alarm rates by 99.98% and 0.14% respectively.
Statistical Prediction for analyzing Epidemiological Characteristics of COVID...Nuwan Sriyantha Bandara
In this presentation, we propose a novel integrated model for analyzing the characteristics of the epidemiological curve of COVID-19 by utilizing an enhanced compartmental statistical prediction model which is developed conferring susceptible-infectious susceptible (SIS) model, susceptible-infectious-removed (SIR) model, Dirichlet process model, and the interpretive structural model.
The oral presentation of the research has been presented at the International Research Conference 2020 of Sri Lanka Technological Campus on 17th June, 2020.
Abstract Link: http://repo.sltc.ac.lk/handle/1/82
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
According to the Nilson report, the global Credit card and debit card fraud resulted in losses amounting to $24.71 billion in 2016 and 72% were bored by the Card issuers. Therefore, the card issue companies are eager to predict the fraud in real time and in advance to reduce their loss and protect their revenue. The goal of the project is to provide fraud analytics for credit card issue companies to predict fraud in real-time and in advance. By building a supervised fraud prediction model, we are aiming to capture the maximum number of real frauds while limiting the occurrence of mis-flagged frauds, in order to achieve a win-win situation both maximize our ROI and achieve customer satisfaction.
An intrusion detection model based on fuzzy membership function using gnpeSAT Journals
Abstract As the Internet facilities increasing over the world, threats, attacks or intrusions over the Internet are also increasing. Therefore, an intrusion detection model is required to detect intrusion that going to threaten CIA of internet resources. A GNP based fuzzy membership function is much more suitable for identifying such kind of intrusions. A GNP which is a combination of GA and GP applied to extract association rules. A combined GNP-fuzzy membership method would help us to extract important association rules from DARPA 98/99 dataset rather than all rules from DARPA 98/99 dataset. Then the extracted association rules would be updated using genetic operations and also stored into rule pool. In classification, association rules will be classified as normal or intrusion based on calculated match degree. The classified association rules will be stored separately in two different rule pools. Normal rules in normal rule pool and intrusion rules in intrusion rule pool. For the new data match degree will be calculated based on available normal rules and intrusion rules. Then this calculated match degree will help us to identify whether the new data normal or intrusion. Keywords: Fuzzy membership function, Genetic network programming, Genetic algorithm, DARPA 98/99 dataset and Intrusion detection.
Data mining is an integrated field, depicted technologies in combination to the areas having database, learning by machine, statistical study, and recognition in patterns of same type, information regeneration, A.I networks, knowledge-based portfolios, artificial intelligence, neural network, and data determination. In real terms, mining of data is the investigation of provisional data sets for finding hidden connections and to gather the information in peculiar form which are justifiable and understandable to the owner of gather or mined data. An unsupervised formula which differentiate data components into collections by which the components in similar group are more allied to one other and items in rest of cluster seems to be non-allied, by the criteria of measurement of equality or predictability is called process of clustering. Cluster analysis is a relegating task that is utilized to identify same group of object and it is additionally one of the most widely used method for many practical application in data mining. It is a method of grouping objects, where objects can be physical, such as a student or may be a summary such as customer comportment, handwriting. It has been proposed many clustering algorithms that it falls into the different clustering methods. The intention of this paper is to provide a relegation of some prominent clustering algorithms.
A predictive model for mapping crime using big data analyticseSAT Journals
Abstract Crime reduction and prevention challenges in today’s world are becoming increasingly complex and are in need of a new technique that can handle the vast amount of information that is being generated. Traditional police capabilities mostly fall short in depicting the original division of criminal activities, thus contribute less in the suitable allocation of police services. In this paper methods are described for crime event forecasting, using Hadoop, by studying the geographical areas which are at greater risk and outside the traditional policing limits. The developed method makes the use of a geographical crime mapping algorithm to identify areas that have relatively high cases of crime. The term used for such places is hot spots. The identified hotspot clusters give valuable data that can be used to train the artificial neural network which further can model the trends of crime. The artificial neural network specification and estimation approach is enhanced by processing capability of Hadoop platform. Keywords— Crime forecasting; Cluster analysis; artificial neural networks; Patrolling; Big data; Hadoop; Gamma test.
Survey of Data Mining Techniques on Crime Data Analysisijdmtaiir
Data mining is a process of extracting knowledge
from huge amount of data stored in databases, data warehouses
and data repositories. Crime is an interesting application where
data mining plays an important role in terms of prediction and
analysis. Clustering is the process of combining data objects
into groups. The data objects within the group are very similar
and very dissimilar as well when compared to objects of other
groups. This paper presents detailed study on clustering
techniques and its role on crime applications. This study also
helps crime branch for better prediction and classification of
crimes
Abstract -Data mining is a process of extracting knowledge from huge amount of data stored in databases, data warehouses and data repositories. Crime is an interesting application where data mining plays an important role in terms of prediction and analysis. Clustering is the process of combining data objects into groups. The data objects within the group are very similar and very dissimilar as well when compared to objects of other groups. This paper presents detailed study on clustering techniques and its role on crime applications. This study also helps crime branch for better prediction and classification of crimes.
4Data Mining Approach of Accident Occurrences Identification with Effective M...IJECEIAES
Data mining is used in various domains of research to identify a new cause for tan effect in the society over the globe. This article includes the same reason for using the data mining to identify the Accident Occurrences in different regions and to identify the most valid reason for happening accidents over the globe. Data Mining and Advanced Machine Learning algorithms are used in this research approach and this article discusses about hyperline, classifications, pre-processing of the data, training the machine with the sample datasets which are collected from different regions in which we have structural and semi-structural data. We will dive into deep of machine learning and data mining classification algorithms to find or predict something novel about the accident occurrences over the globe. We majorly concentrate on two classification algorithms to minify the research and task and they are very basic and important classification algorithms. SVM (Support vector machine), CNB Classifier. This discussion will be quite interesting with WEKA tool for CNB classifier, Bag of Words Identification, Word Count and Frequency Calculation.
Analysis of Crime Big Data using MapReduceKaushik Rajan
Analyzed Crime Big data of Washington DC to solve the following business queries:
> Which hour has the highest crime count?
> Which shift has the highest crime count?
> Year wise crime count
> Hour wise crime count
> Crime count by an offense
> Average of Shift wise crime count
The data was initially stored in MySql which was then moved to HDFS using SQOOP, from where 4 MapReduce operations are doing using JAVA in Eclipse IDE. The outputs of the queries are then moved to HBase using SQOOP. Two more MapReduce operations are done using PIG, the output of which is also moved to HBase using SQOOP. All the outputs were then moved to the local system and are visualized using RStudio and Tableau.
Tools used:
> MySQL, HDFS and HBase to store the data
> SCOOP to move the data from one database to another
> JAVA (Eclipse IDE) and PIG to run the MapReduce queries
> RStudio for data pre-processing and visualization
> Tableau for visualization
> LATEX for Documentation
SUPERVISED AND UNSUPERVISED MACHINE LEARNING METHODOLOGIES FOR CRIME PATTERN ...ijaia
Crime is a grave problem that affects all countries in the world. The level of crime in a country has a big
impact on its economic growth and quality of life of citizens. In this paper, we provide a survey of trends of
supervised and unsupervised machine learning methods used for crime pattern analysis. We use a spatiotemporal dataset of crimes in San Francisco, CA to demonstrate some of these strategies for crime
analysis. We use classification models, namely, Logistic Regression, Random Forest, Gradient Boosting
and Naive Bayes to predict crime types such as Larceny, Theft, etc. and propose model optimization
strategies. Further, we use a graph based unsupervised machine learning technique called core periphery
structures to analyze how crime behavior evolves over time. These methods can be generalized to use for
different counties and can be greatly helpful in planning police task forces for law enforcement and crime
prevention.
Supervised and Unsupervised Machine Learning Methodologies for Crime Pattern ...gerogepatton
Crime is a grave problem that affects all countries in the world. The level of crime in a country has a big
impact on its economic growth and quality of life of citizens. In this paper, we provide a survey of trends of
supervised and unsupervised machine learning methods used for crime pattern analysis. We use a spatiotemporal dataset of crimes in San Francisco, CA to demonstrate some of these strategies for crime
analysis. We use classification models, namely, Logistic Regression, Random Forest, Gradient Boosting
and Naive Bayes to predict crime types such as Larceny, Theft, etc. and propose model optimization
strategies. Further, we use a graph based unsupervised machine learning technique called core periphery
structures to analyze how crime behavior evolves over time. These methods can be generalized to use for
different counties and can be greatly helpful in planning police task forces for law enforcement and crime
prevention.
News document analysis by using a proficient algorithmIJERA Editor
News articles analyzing is one of the emerging research topic in the past few years. News paper discusses various types (political, education, employment, sports, agriculture, crime, medicine, business, etc) of news in different levels such as International, National, state and district level. In this news articles, crime discussion plays a major role because one crime leads to a many other crimes and also affect many other lives. In India, Madurai is one of the important places which have many historical monuments. Madurai is a sensitive place. This paper analyzes the crimes which occur in the year 2015 in and around Madurai. This analysis helps to police department to reduce the occurrence of crime in the future. This proposed system used Support Vector Machine (SVM) for effectively classify the document. News documents are preprocessed using pruning and stemming. From the stemmed words, the informative words are selected and weighted using feature selection methods such as Term-Frequency and Inverse Document Frequency (TF-IDF) and Chi-square. It returns the high dimensional vector space. It is reduced to low dimension using Latent Semantic Analysis (LSA) method. Compute the cosine similarity between the key document and news documents. Based on the value, the news documents are labeled as crime and non-crime. Some of the documents are used to train the SVM classifier. Some of the documents are used to test the performance of developed system. From the comparative study, it is identified that the performance of the proposed approach improves the classification accuracy.
ISSN 2395-650X
The "International Journal of Life Sciences Biotechnology and Pharma Sciences journal appears to be a valuable resource for those interested in staying updated on the latest developments and research in these important scientific fields of Life and science journal.
Analysis on different Data mining Techniques and algorithms used in IOTIJERA Editor
In this paper, we discusses about five functionalities of data mining in IOT that affects the performance and that
are: Data anomaly detection, Data clustering, Data classification, feature selection, time series prediction. Some
important algorithm has also been reviewed here of each functionalities that show advantages and limitations as
well as some new algorithm that are in research direction. Here we had represent knowledge view of data
mining in IOT.
Maintaining Data Confidentiality in Association Rule Mining in Distributed En...IJSRD
The data in real world applications is distributed at multiple locations, and the owner of the databases may be different people. Thus to perform mining task, the data needs to be kept at central location which causes threat to the privacy of corporate data. Hence the key challenge is to applying mining on distributed source data with preserving privacy of corporate data. The system addresses the problem of incrementally mining frequent itemsets in dynamic environment. The assumption made here is that, after initial mining the source undergoes into small changes in each time. The privacy of data should not be threatened by an adversary i.e. the miner and target database owner should not be able to recover original data from transformed data.
Performance and Emission characteristics of a Single Cylinder Four Stroke Die...IJSRD
The current trends in CI engine are to use Water-diesel emulsion as alternative fuel. It can be employed directly to the existing CI Engine system with no additional modifications. This system helps in reduction of NOx as well as PM, which in turn improve the combustion efficiency of the engine. However there are still investigations have to be done. The current work mainly concentrated on diesel engine run on water-diesel emulsions and its effect on engine performance and emissions were studied. The various loads were applied on a constant speed diesel engine run on water-diesel emulsions of varying ratios of 0.2:1, 0.3:1. 0.4:1 and 0.5:1. Emission and performance characteristics were measured and were compared with base diesel operation. The emissions like NOx and smoke density were found to decrease greatly and brake thermal efficiency was found to increase at high loads. Smoke level was 4.2 BSU and 3 BSU for base diesel and water diesel emulsion of 0.4. The ignition delay was found to increase with water diesel emulsions. This also increased the maximum rate of pressure rise and peak pressure. The engine was found to run rough with water-diesel emulsions. The optimal water-diesel ratio was found to be 0.4:1 by weight. HC and CO emissions were found to increase with water diesel emulsions.
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A Survey on Data Mining Techniques for Crime Hotspots Prediction
1. IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 10, 2015 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 1112
A Survey on Data Mining Techniques for Crime Hotspots Prediction
Neha Patel1 Prof. Shivani V. Vora2
1
P.G. Student 2
Assistant Professor
1,2
Department of Computer Engineering
1,2
CGPIT, UkaTarsadiya University, Mahuva, Surat, Gujarat, India.
Abstract— A crime is an act which is against the laws of a
country or region. The technique which is used to find areas
on a map which have high crime intensity is known as crime
hotspot prediction. The technique uses the crime data which
includes the area with crime rate and predict the future
location with high crime intensity. The motivation of crime
hotspot prediction is to raise people’s awareness regarding
the dangerous location in certain time period. It can help for
police resource allocation for creating a safe environment.
The paper presents survey of different types of data mining
techniques for crime hotspots prediction.
Key words: Data mining; crime hotspot; prediction;
accuracy
I. INTRODUCTION
Crime is a social problem that affecting the people’s life and
economic development of a society. The prediction of crime
is difficult [1]. The occurrence of crime is related to a
variety of socio-economic and crime opportunity factors,
like population, economic investment and arrest rate.
Crime usually has spatial and temporal
characteristics related to the population, environment,
economic factors, politics, and social events. The victims of
crime may not be predicted but the place that has probability
of an occurrence of crime it may be predicted. Analysis of
large amount of crime data is a difficult task. Data mining is
a useful process to handle amount of data and to reduce the
manpower. The most effective spatial-temporal analysis for
the understanding of the contained relationships among
crime events is the analysis of crime hot spots. The analysis
of crime hot spots contributes to the conflict and prevention
of crimes by allowing the planning of strategies that
optimize the distribution of police resources. As police
resources are limited in some areas, the planning of such
allocation becomes an important task. Two type of crime
hotspots are there: crime general and crime specific. Wide
range of crime types are occur in a particular area then is
known as crime general hotspot. One or several types of
crimes are occur in a particular area then it is called as a
crime specific hotspot. The crime hotspots prediction is
done using crime location, crime time and which type of
crime is done. We can also find the area for specific crime.
The main motivation of crime hotspots prediction is to
create a safe environment and for police resource allocation.
In the next section-II the general steps for crime
hotspots prediction and classification techniques are
introduced. Where five classification techniques are
described. Section-III represents the comparative analysis of
three classification techniques which are support vector
machine, decision tree and naïve bays.
II. LITERATURE SURVEY
The general steps for crime hotspot prediction are:
1)Data collection
2)Preprocessing
3)Feature selection
4)Classification
5)Prediction
6)Visualization
A. Data Collection
In data collection step crime data is collected from different
sources like news sites, blogs, RSS feed and mainly from
police records. For unstructured data Mongo database is
used [2].
B. Preprocessing
There are few techniques for data preprocessing. This
techniques are data cleaning, reduction, integration,
discretization, transformation and feature selection. It
intends to reduce some noises, incomplete and in consist
data.
The data cleaning is used to decrease noise and
handle missing values. There are a number of methods for
handling records that contain missing values such as
omitting the incorrect fields(s) or entire record that contains
the incorrect field(s), automatically entering or correcting
the data with default values, deriving a model to enter or
correct the data, replacing all values with a global constant
and using the imputation method to predict missing values.
Fig. 1: steps for crime hotspot prediction [2][3].
Data reduction is necessary to remove irrelevant
attributes from dataset. For example according to Almanie,
Mirza and Lor [4], the authors performed data reduction in
terms of number of instances. They observed Denver crimes
dataset contained a set of traffic accident instances. The
attribute “Is_Crime” suggests whether the instance belongs
to a crime or accident. The author used the attribute
2. A Survey on Data Mining Techniques for Crime Hotspots Prediction
(IJSRD/Vol. 3/Issue 10/2015/258)
All rights reserved by www.ijsrd.com 1113
“Is_Crime” to filter the instances and remove all the
irrelevant ones [4].
Data integration steps is used for different
purposes. In Almanie, Mirza and Lor [4] the authors used to
avoid different attribute naming, they unified the key
attribute names for both crime datasets as follow:
Crime_Type, Crime_Date and Crime_Location.
Crime_Location represents the neighborhood attribute for
Denver dataset whereas the Area attribute for Los Angeles
dataset.
The data transformation is used to reduce the
diversity of attribute values by mapping their values to fall
within smaller group. For example burglary and robbery
crimes are included in theft crime type [4].
C. Feature Selection
Feature selection is a part of data preprocessing. Feature
selection is used to remove the irrelevant or redundant
attributes. Feature selection has several objectives such as
enhancing model performance by avoiding over fitting in
the case of supervised classification [1]. The main attributes
like crime type, location, crime time in feature selection
process.
D. Classification
After preprocessing and feature selection phases, the
numbers of attribute was meaningfully extract and are now
more precise for building the data mining models.
Classification as a famous data mining supervised learning
techniques are used to extract meaningful information from
large datasets and can be adequately used to predict
unknown classes. There are various classification
algorithms, such as Support Vector Machines (SVM), k-
Nearest Neighbor (k-NN), Decision Tree, Weighted Voting
and Artificial Neural Networks. All these techniques can be
applied to a dataset for discovering sets of models to
forecast unknown class labels [1][3].
E. Prediction
In order to quantitatively predict the crime status, many data
mining methods can be used. In this study, a classification
task is applied for prediction.
F. Visualization
The crime prone areas can be graphically represented using
a heat map which indicates level of activity, generally
darker colors to represents low activity and brighter colors
to represents high activity [1][2][4].
The steps for crime hotspots prediction are
introduced. Now the survey of different data mining
techniques for prediction are presented. The objective of
prediction is to forecast the value of an attribute based on
value of other attributes. In the prediction techniques first a
model is created based on data distribution and then that
model is used to predict future on unknown value. The basic
data mining techniques are introduced which are used to
predict crime hotspots.
1) Support Vector Machine
The support vector machines are supervised learning models
with associated learning algorithms that analyze the data and
recognize the patterns that is used for classification and
regression analysis. A support vector machine construct a
hyperplane or set of hyperplanes in a high or infinite-
dimensional space, which can be used for classification,
regression and other tasks.
The support vector machine technique is used for support
vector machine [3]. The following approach is: The data
used for this research was taken from a variety of city
agencies. Each data contains the type of event, the location
with longitude and latitude, and the time and date of the
incident. This data was classified from different
classification techniques. The area which have the crime rate
above the predefined rate are positive or members of hotspot
class and area with crime rate below the predefined rate are
negative or non-members of hotspot class. This labelled data
set used as the training set in SVM classification. The
technique gives good results in most cases but it is
computationally expensive so it runs slow.
2) Naïve Bayesian
Naïve Bayes classifier is a supervised learning algorithm. It
is effective and widely used. It is a statistical model that
predicts class membership probabilities based on Bayes’
theorem [5]. The naïve bayes classifier model is fast to
build. It can be modified with new training data without
having rebuild the model. This classifier is very simple to
construct and it may be easily apply to huge data sets [1].
Predictive accuracy is generally of this classifier in most
cases. It consider each attributes separately when classify
new instance. It based on the Bayes rule of conditional
probability [6][5].
P (H|X) = P (X|H) P (H)/ P (X)
Where,
1) P (A) is the prior probability of A. It is "prior" in the
sense that it does not take into account any information
about B.
2) P (A|B) is the conditional probability of A, given B. It is
also called the posterior probability because it is derived
from or depends on the specified value of B.
3) P (B|A) is the conditional probability of B given A. It is
also called the likelihood.
3) Decision Tree
Decision tree is a flow chart like structure in which each
internal node represents a ‘test’ on the attribute, each branch
represent outcome of the test and each leaf node represents a
class label. It is simple to understand and to interpret. It is
able to handle both numerical and categorical data. It is also
able to handle multi-output problems. It performs well even
if its assumptions are somewhat violated by the true model
from which the data was generated. For crime hotspot
prediction generally J48 algorithm is used [2][3][4].
The decision tree can be unstable because small
variations in data might results in completely different tree
being generated. Another disadvantage is its complexity [6].
4) Artificial Neural Network
In 1943, McCulloch and Pitts, gives the first model of
artificial neuron. According to Nigrin, A neural network is a
circuit composed of a very large numbers of processing
elements that know as Neuron. Each element works only on
local information. Furthermore each element operates non-
parellerliy, thus there is no system clock [7]. The neural
network have high strength when modeling a complex
system. It gives higher accuracy when increasing the data
but sudden changes in new data might give low results [4].
It takes long running time [6].
3. A Survey on Data Mining Techniques for Crime Hotspots Prediction
(IJSRD/Vol. 3/Issue 10/2015/258)
All rights reserved by www.ijsrd.com 1114
5) K- Nearest Neighbor
The k-Nearest Neighbors algorithm (or k-NN for short) is a
non-parametric method used for classification and
regression [8]. In this technique the classification is done by
comparing feature vectors of the different points. Nearest
neighbor classifier makes their predictions based on local
information, whereas decision tree and rule based classifiers
attempts to find a global model that fits the entire input
space. Because the classification decision are made locally.
This classifier can produce wrong predictions unless the
approximate proximity measure and data preprocessing
steps are taken [6].
In the next section, observation of survey are presented.
III. COMPARATIVE ANALYSIS
Parameters
Support vector
machine
Decision tree Naïve Bayes
Summary
Supervised
learning model.
Analyze data
recognize
patterns [9].
Flowchart like structure in which each
internal node represents a “test” on
attribute, branch as an outcome of taste
and leaf as a class label [1].
Supervised learning algorithm. It is a
statistical model that predicts class
membership probabilities based on
Bayes' theorem [4].
Accuracy
Crime dataset
US(Northeast)[4] 70% to 80%[4] 60% to 70%[4] 70% to 80%[4]
Crime data set of
Denver[4] _ 42%[4] 51%[4]
Crime data set of
Los Angeles[4]
43%[4] 54%[4]
F1-measure
Crime dataset
US(Northeast)[4] 70% to 80%[4] 70% to 80%[4] Above 80%[4]
Table 1. Comparative analysis for data mining techniques.
Table 1 shows the comparative analysis of different data
mining techniques. All techniques are compared with its
prediction accuracy and f1-measure. The support vector
machine provide good accuracy but it is computationally
expensive thus runs slow. The complexity of decision tree is
high. Small variations in data might results in completely
different tree being generated. The Naïve bays is highly
scalable and easy to implement. Good results obtained in
most cases. The results are also depends on which type of
data is given. Here the naïve bays gives high accuracy and
f1-score for different crime database compared to support
vector machine and decision tree.
IV. CONCLUSION
The data mining techniques for predicting crime hotspots
are discuss in this paper. This techniques are capable to
enhance the prediction accuracy, performance and speed.
After analyzing different results from various paper we can
conclude that Naïve Bayes gives efficient results for crime
hotspot prediction.
V. AKNOWLWDGEMENT
I take the opportunity to express my gratitude and regards to
my guide Prof.Shivani Vora, CE & IT Dept., CGPIT,
Bardoli for her suggestions and encouragements.
REFERENCES
[1] Somayeh Shojaee,Aida Mustapha, Fatimah Sidi,
Marzanah A.Jabar, “A study on Classification Learning
algorithms to predict crime status”, International
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