Relevant bibliographies by topics / Criminal statistics Data mining. Association rule mining

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Academic literature on the topic 'Criminal statistics Data mining. Association rule mining'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Criminal statistics Data mining. Association rule mining"

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P.Raja Rajeswari, Dr, P. Surya Teja, P. Sri Harsha, and T. Chaitanya Kumar. "Enhancing the Performance of Crime Prediction Technique Using Data Mining." International Journal of Engineering & Technology 7, no. 2.32 (2018): 424. http://dx.doi.org/10.14419/ijet.v7i2.32.15731.

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Time is a important factor for criminal sentencing. Most of the times, criminal released on the bail which may harmful to the society, even after they have furnished the judgment. This sort of threats cut down through the prediction analysis. The analysis can be done on the concerned person to analyse that she/ he is about to do the crime. So that, its benefit not only for law enforcement but also for country safety. Data Mining, a method which handles a massive datasets. Data mining also used to guess desired patterns. Police Officers are the best persons for crime prediction and also predict the criminal’s upcoming activities. We are here implementing the use of Frequent Mining Pattern in addition to Association Rule Mining. The main aim of this paper is analysing several crimes by various criminals and to predict chance of crimes by same criminal. It will be helpful for Country Law Enforcement and safeguarding from criminals who were released on Bail. Aim can be achieved with the help of Apriori Algorithm.
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Wu, You, Zheng Wang, and Shengqi Wang. "Human Resource Allocation Based on Fuzzy Data Mining Algorithm." Complexity 2021 (June 10, 2021): 1–11. http://dx.doi.org/10.1155/2021/9489114.

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Data mining is currently a frontier research topic in the field of information and database technology. It is recognized as one of the most promising key technologies. Data mining involves multiple technologies, such as mathematical statistics, fuzzy theory, neural networks, and artificial intelligence, with relatively high technical content. The realization is also difficult. In this article, we have studied the basic concepts, processes, and algorithms of association rule mining technology. Aiming at large-scale database applications, in order to improve the efficiency of data mining, we proposed an incremental association rule mining algorithm based on clustering, that is, using fast clustering. First, the feasibility of realizing performance appraisal data mining is studied; then, the business process needed to realize the information system is analyzed, the business process-related links and the corresponding data input interface are designed, and then the data process to realize the data processing is designed, including data foundation and database model. Aiming at the high efficiency of large-scale database mining, database development tools are used to implement the specific system settings and program design of this algorithm. Incorporated into the human resource management system of colleges and universities, they carried out successful association broadcasting, realized visualization, and finally discovered valuable information.
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Saltos, Ginger, and Mihaela Cocea. "An Exploration of Crime Prediction Using Data Mining on Open Data." International Journal of Information Technology & Decision Making 16, no. 05 (2017): 1155–81. http://dx.doi.org/10.1142/s0219622017500250.

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The increase in crime data recording coupled with data analytics resulted in the growth of research approaches aimed at extracting knowledge from crime records to better understand criminal behavior and ultimately prevent future crimes. While many of these approaches make use of clustering and association rule mining techniques, there are fewer approaches focusing on predictive models of crime. In this paper, we explore models for predicting the frequency of several types of crimes by LSOA code (Lower Layer Super Output Areas — an administrative system of areas used by the UK police) and the frequency of anti-social behavior crimes. Three algorithms are used from different categories of approaches: instance-based learning, regression and decision trees. The data are from the UK police and contain over 600,000 records before preprocessing. The results, looking at predictive performance as well as processing time, indicate that decision trees (M5P algorithm) can be used to reliably predict crime frequency in general as well as anti-social behavior frequency.
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Sekhar Babu, B., P. Lakshmi Prasanna, and P. Vidyullatha. "Personalized web search on e-commerce using ontology based association mining." International Journal of Engineering & Technology 7, no. 1.1 (2017): 286. http://dx.doi.org/10.14419/ijet.v7i1.1.9487.

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In current days, World Wide Web has grown into a familiar medium to investigate the new information, Business trends, trading strategies so on. Several organizations and companies are also contracting the web in order to present their products or services across the world. E-commerce is a kind of business or saleable transaction that comprises the transfer of statistics across the web or internet. In this situation huge amount of data is obtained and dumped into the web services. This data overhead tends to arise difficulties in determining the accurate and valuable information, hence the web data mining is used as a tool to determine and mine the knowledge from the web. Web data mining technology can be applied by the E-commerce organizations to offer personalized E-commerce solutions and better meet the desires of customers. By using data mining algorithm such as ontology based association rule mining using apriori algorithms extracts the various useful information from the large data sets .We are implementing the above data mining technique in JAVA and data sets are dynamically generated while transaction is processing and extracting various patterns.
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Nithya, NS, and K. Duraiswamy. "Correlated gain ratio based fuzzy weighted association rule mining classifier for diagnosis health care data." Journal of Intelligent & Fuzzy Systems 29, no. 4 (2015): 1453–64. http://dx.doi.org/10.3233/ifs-151614.

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Yoseph, Fahed, and Markku Heikkilä. "A new approach for association rules mining using computational and artificial intelligence." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 7233–46. http://dx.doi.org/10.3233/jifs-200707.

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Market Intelligence is knowledge extracted from numerous data sources, both internal and external, to provide a holistic view of the market and to support decision-making. Association Rules Mining provides powerful data mining techniques for identifying associations and co-occurrences in large databases. Market Basket Analysis (MBA) uses ARM to gain insights from heterogeneous consumer shopping patterns and examines the effects of marketing initiatives. As Artificial Intelligence (AI) more and more finds its way to marketing, it entails fundamental changes in the skills-set required by marketers. For MBA, AI provides important ways to improve both the outcomes of the market basket analysis and the performance of the analysis process. In this study we demonstrate the effects of AI on MBA by our proposed new MBA model where results of computational intelligence are used in data preprocessing, in market segmentation and in finding market trends. We show with point-of-sale (POS) data of a small, local retailer that our proposed “Åbo algorithm” MBA model increases mining performance/intelligence and extract important marketing insights to assess both demand dynamics and product popularity trends. Additionally, the results show how, as related to the 80/20 percent rule, 78% of revenue is derived 16% of the product assortment.
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Tseng, Ming-Hseng, and Hui-Ching Wu. "Investigating Health Equity and Healthcare Needs among Immigrant Women Using the Association Rule Mining Method." Healthcare 9, no. 2 (2021): 195. http://dx.doi.org/10.3390/healthcare9020195.

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Equitable access to healthcare services is a major concern among immigrant women. Thus, this study investigated the relationship between socioeconomic characteristics and healthcare needs among immigrant women in Taiwan. The secondary data was obtained from “Survey of Foreign and Chinese Spouses’ Living Requirements, 2008”, which was administered to 5848 immigrant women by the Ministry of the Interior, Taiwan. Additionally, descriptive statistics and significance tests were used to analyze the data, after which the association rule mining algorithm was applied to determine the relationship between socioeconomic characteristics and healthcare needs. According to the findings, the top three healthcare needs were providing medical allowances (52.53%), child health checkups (16.74%), and parental knowledge and pre- and post-natal guidance (8.31%). Based on the association analysis, the main barrier to the women’s healthcare needs was “financial pressure”. This study also found that nationality, socioeconomic status, and duration of residence were associated with such needs, while health inequality among aged immigrant women was due to economic and physical factors. Finally, the association analysis found that the women’s healthcare problems included economic, socio-cultural, and gender weakness, while “economic inequality” and “women’s health” were interrelated.
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Cheng, Haodong, Meng Han, Ni Zhang, Xiaojuan Li, and Le Wang. "A Survey of incremental high-utility pattern mining based on storage structure." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 841–66. http://dx.doi.org/10.3233/jifs-202745.

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Traditional association rule mining has been widely studied, but this is not applicable to practical applications that must consider factors such as the unit profit of the item and the purchase quantity. High-utility itemset mining (HUIM) aims to find high-utility patterns by considering the number of items purchased and the unit profit. However, most high-utility itemset mining algorithms are designed for static databases. In real-world applications (such as market analysis and business decisions), databases are usually updated by inserting new data dynamically. Some researchers have proposed algorithms for finding high-utility itemsets in dynamically updated databases. Different from the batch processing algorithms that always process the databases from scratch, the incremental HUIM algorithms update and output high-utility itemsets in an incremental manner, thereby reducing the cost of finding high-utility itemsets. This paper provides the latest research on incremental high-utility itemset mining algorithms, including methods of storing itemsets and utilities based on tree, list, array and hash set storage structures. It also points out several important derivative algorithms and research challenges for incremental high-utility itemset mining.
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Wang, Liwen, and Soo-Jin Chung. "Sustainable Development of College and University Education by use of Data Mining Methods." International Journal of Emerging Technologies in Learning (iJET) 16, no. 05 (2021): 102. http://dx.doi.org/10.3991/ijet.v16i05.20303.

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To improve the education efficiency of the students, the student-centered education plan is explored. First, the Apriori algorithm of association rules is used to mine the potential related patterns in the score data of college students and establish a reasonable teaching method. Second, aided by the decision tree model, the factors affecting students' academic performance are studied, and the potential relationship between different courses is studied. Finally, the Apriori algorithm of association rules combined with decision tree model is used to generate the early warning mechanism of students' achievement, and the course performance of college students is empirically analyzed. The results show that: C language has two sides of dependence on many subjects; higher mathematics → linear algebra → mathematical statistics → computer composition principle → computer network. The teaching scheme of C language → C + + → Java more conforms to the learning mechanism of college students. Through empirical analysis, the early warning mechanism of association rule Apriori algorithm and decision tree model can effectively analyze student's course and give student's achievement. It is found that the method proposed can provide theoretical basis for students, teachers, and university administrators to carry out education reform and education management decision-making, improve students' performance and education quality, and realize the "student-oriented" education concept, so it can be applied to the actual education management.
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Senthil, D., and G. Suseendran. "Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine." International Journal of Engineering & Technology 7, no. 3.3 (2018): 218. http://dx.doi.org/10.14419/ijet.v7i2.33.13890.

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Time series analysis is an important and complex problem in machine learning and statistics. In the existing system, Support Vector Machine (SVM) and Association Rule Mining (ARM) is introduced to implement the time series data. However it has issues with lower accuracy and higher time complexity. Also it has issue with optimal rules discovery and segmentation on time series data. To avoid the above mentioned issues, in the proposed research Sliding Window Technique based Improved ARM with Enhanced SVM (SWT-IARM with ESVM) is proposed. In the proposed system, the preprocessing is performed using Modified K-Means Clustering (MKMC). The indexing process is done by using R-tree which is used to provide faster results. Segmentation is performed by using SWT and it reduces the cost complexity by optimal segments. Then IARM is applied on efficient rule discovery process by generating the most frequent rules. By using ESVM classification approach, the rules are classified more accurately.
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Dissertations / Theses on the topic "Criminal statistics Data mining. Association rule mining"

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Padhye, Manoday D. "Use of data mining for investigation of crime patterns." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4836.

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Thesis (M.S.)--West Virginia University, 2006.<br>Title from document title page. Document formatted into pages; contains viii, 108 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 80-81).
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Schweickart, Ian R. W. "Investigating Post-Earnings-Announcement Drift Using Principal Component Analysis and Association Rule Mining." Scholarship @ Claremont, 2017. https://scholarship.claremont.edu/hmc_theses/94.

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Post-Earnings-Announcement Drift (PEAD) is commonly accepted in the fields of accounting and finance as evidence for stock market inefficiency. Less accepted are the numerous explanations for this anomaly. This project aims to investigate the cause for PEAD by harnessing the power of machine learning algorithms such as Principle Component Analysis (PCA) and a rule-based learning technique, applied to large stock market data sets. Based on the notion that the market is consumer driven, repeated occurrences of irrational behavior exhibited by traders in response to news events such as earnings reports are uncovered. The project produces findings in support of the PEAD anomaly using non-accounting nor financial methods. In particular, this project finds evidence for delayed price response exhibited in trader behavior, a common manifestation of the PEAD phenomenon.
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Book chapters on the topic "Criminal statistics Data mining. Association rule mining"

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Kacprzyk, Janusz, and Sławomir Zadrożny. "Derivation of Linguistic Summaries is Inherently Difficult: Can Association Rule Mining Help?" In Towards Advanced Data Analysis by Combining Soft Computing and Statistics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30278-7_23.

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Gürbüz, Feyza, and Fatma Gökçe Önen. "Informational Data Mining." In Enterprise Business Modeling, Optimization Techniques, and Flexible Information Systems. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3946-1.ch005.

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The previous decades have witnessed major change within the Information Systems (IS) environment with a corresponding emphasis on the importance of specifying timely and accurate information strategies. Currently, there is an increasing interest in data mining and information systems optimization. Therefore, it makes data mining for optimization of information systems a new and growing research community. This chapter surveys the application of data mining to optimization of information systems. These systems have different data sources and accordingly different objectives for knowledge discovery. After the preprocessing stage, data mining techniques can be applied on the suitable data for the objective of the information systems. These techniques are prediction, classification, association rule mining, statistics and visualization, clustering and outlier detection.
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Breitenbach, Markus, William Dieterich, Tim Brennan, and Adrian Fan. "Creating Risk-Scores in Very Imbalanced Datasets." In Rare Association Rule Mining and Knowledge Discovery. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-754-6.ch015.

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In this chapter, the authors explore Area under Curve (AUC) as an error-metric suitable for imbalanced data, as well as survey methods of optimizing this metric directly. We also address the issue of cut-point thresholds for practical decision-making. The techniques will be illustrated by a study that examines predictive rule development and validation procedures for establishing risk levels for violent felony crimes committed when criminal offenders are released from prison in the USA. The “violent felony” category was selected as the key outcome since these crimes are a major public safety concern, have a low base-rate (around 7%), and represent the most extreme forms of violence. The authors compare the performance of different algorithms on the dataset and validate using survival analysis whether the risk scores produced by these techniques are computing reasonable estimates of the true risk.
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