scholarly journals Applications of Machine Learning in Public Security Information and Resource Management

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhihui Wang ◽  
Jinyu Wang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Warehouse ◽  
Large Scale ◽  
Relevant Information ◽  
Practical Significance ◽  
Public Security ◽  
Data Mining Algorithms ◽  
New Findings ◽  
Security Information

The data mining and big data technologies could be of utmost importance to investigate outbound and case datasets in the police records. New findings and useful information may potentially be obtained through data preprocessing and multidimensional modeling. Public security data is a kind of “big data,” having characteristics like large volume, rapid growth, various structures, large-scale storage, low density, and time sensitiveness. In this paper, a police data warehouse is constructed and a public security information analysis system is proposed. The proposed system comprises two modules: (i) case management and (ii) public security information mining. The former is responsible for the collection and processing of case information. The latter preprocesses the data of major cases that have occurred in the past ten years to create a data warehouse. Then, we use the model to create a data warehouse based on needs. By dividing the measurement values and dimensions, the analysis and prediction of criminals’ characteristics and the case environment realize relationships between them. In the process of mining and processing crime data, data mining algorithms can quickly find out the relevant information in the data. Furthermore, the system can find out relevant trends and laws to detect criminal cases faster than other methods. This can reduce the emergence of new crimes and provide a basis for decision-making in the public security department that has practical significance.

scholarly journals Migrating From Data Mining to Big Data Mining

2018 ◽  
Vol 7 (3.4) ◽  
pp. 13
Author(s):  
Gourav Bathla ◽  
Himanshu Aggarwal ◽  
Rinkle Rani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Response Time ◽  
Large Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Mining Algorithm ◽  
Big Data Mining ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Data mining is one of the most researched fields in computer science. Several researches have been carried out to extract and analyse important information from raw data. Traditional data mining algorithms like classification, clustering and statistical analysis can process small scale of data with great efficiency and accuracy. Social networking interactions, business transactions and other communications result in Big data. It is large scale of data which is not in competency for traditional data mining techniques. It is observed that traditional data mining algorithms are not capable for storage and processing of large scale of data. If some algorithms are capable, then response time is very high. Big data have hidden information, if that is analysed in intelligent manner can be highly beneficial for business organizations. In this paper, we have analysed the advancement from traditional data mining algorithms to Big data mining algorithms. Applications of traditional data mining algorithms can be straight forward incorporated in Big data mining algorithm. Several studies have analysed traditional data mining with Big data mining, but very few have analysed most important algortihsm within one research work, which is the core motive of our paper. Readers can easily observe the difference between these algorthithms with  pros and cons. Mathemtics concepts are applied in data mining algorithms. Means and Euclidean distance calculation in Kmeans, Vectors application and margin in SVM and Bayes therorem, conditional probability in Naïve Bayes algorithm are real examples.  Classification and clustering are the most important applications of data mining. In this paper, Kmeans, SVM and Naïve Bayes algorithms are analysed in detail to observe the accuracy and response time both on concept and empirical perspective. Hadoop, Mapreduce etc. Big data technologies are used for implementing Big data mining algorithms. Performace evaluation metrics like speedup, scaleup and response time are used to compare traditional mining with Big data mining.  

scholarly journals Analysis of University Students’ Behavior Based on a Fusion K-Means Clustering Algorithm

2020 ◽  
Vol 10 (18) ◽  
pp. 6566
Author(s):  
Wenbing Chang ◽  
Xinpeng Ji ◽  
Yinglai Liu ◽  
Yiyong Xiao ◽  
Bang Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Learning Performance ◽  
Practical Significance ◽  
Digital Campus ◽  
Data Mining Algorithms ◽  
Density Peaks ◽  
Data Points ◽  
The University

With the development of big data technology, creating the ‘Digital Campus’ is a hot issue. For an increasing amount of data, traditional data mining algorithms are not suitable. The clustering algorithm is becoming more and more important in the field of data mining, but the traditional clustering algorithm does not take the clustering efficiency and clustering effect into consideration. In this paper, the algorithm based on K-Means and clustering by fast search and find of density peaks (K-CFSFDP) is proposed, which improves on the distance and density of data points. This method is used to cluster students from four universities. The experiment shows that K-CFSFDP algorithm has better clustering results and running efficiency than the traditional K-Means clustering algorithm, and it performs well in large scale campus data. Additionally, the results of the cluster analysis show that the students of different categories in four universities had different performances in living habits and learning performance, so the university can learn about the students’ behavior of different categories and provide corresponding personalized services, which have certain practical significance.

scholarly journals Data Mining Algorithm for Physical Health Monitoring of Young Students Based on Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jiangang Sun ◽  
Xiaoran Jiang ◽  
Guoliang Yuan ◽  
Zhenhuai Chen
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Physical Health ◽  
Data Management System ◽  
Practical Significance ◽  
Data Mining Algorithm ◽  
Physical Test ◽  
Healthy Development ◽  
Data Mining Algorithms ◽  
Mining Algorithm

With the continuous improvement of living standards, the level of physical development of adolescents has improved significantly. The physical functions and healthy development of adolescents are relatively slow and even appear to decline. This paper proposes a novel data mining algorithm based on big data for monitoring of adolescent student’s physical health to overcome this problem and enhance young people’s physical fitness and mental health. Since big data technology has positive practical significance in promoting young people’s healthy development and promoting individual health rights, this article will implement commonly used data mining algorithms and Hadoop/Spark big data processing. The algorithm on different platforms verified that the big data platform has good computing performance for the data mining algorithm by comparing the running time. The current work will prove to be a complete physical health data management system and effectively save, process, and analyze adolescents’ physical test data.

scholarly journals Novel IT Technologies on the Digital Battlefield: The Application of Big Data and Data Mining Technologies

Hadmérnök ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. 141-158
Author(s):  
Eszter Katalin Bognár
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Processing ◽  
Relevant Information ◽  
Military Operations ◽  
Textual Data ◽  
Modern Warfare ◽  
Tools And Techniques

In modern warfare, the most important innovation to date has been the utilisation of information as a  weapon. The basis of successful military operations is  the ability to correctly assess a situation based on  credible collected information. In today’s military, the primary challenge is not the actual collection of data.  It has become more important to extract relevant  information from that data. This requirement cannot  be successfully completed without necessary  improvements in tools and techniques to support the acquisition and analysis of data. This study defines  Big Data and its concept as applied to military  reconnaissance, focusing on the processing of  imagery and textual data, bringing to light modern  data processing and analytics methods that enable  effective processing.

scholarly journals A Pattern Storage System using Pattern Warehouse along with Sources of Pattern Generation and Applications

2019 ◽  
Vol 8 (10S) ◽  
pp. 357-362
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Storage System ◽  
Critical Evaluation ◽  
Large Data ◽  
Vital Role ◽  
Pattern Generation ◽  
Data Repository ◽  
Storage Unit ◽  
Data Mining Algorithms

Now a day different data mining algorithms are ready to create the specific set of data known as Pattern from a huge data repository, but there is no infrastructure or system to save it as persistent storage for the generated patterns. Pattern warehouse presents a foundation to make these patterns safe in the specific environment for long term use. Most organizations are excited to know the information or patterns rather than raw data or group of unprocessed data. Because extracted knowledge play a vital role to take right decision for the growth of an organization. We have examined the sources of patterns generated from large data sets. In this paper, we have presented little importance on the application area of pattern and idea of patter warehouse, the architecture of pattern warehouse then correlation between data warehouse and data mining, association between data mining and pattern warehouse, critical evaluation between existing approaches which theoretically published and more stress on association rule related review elements. In this paper, we analyze the patterns warehouse, data warehouse concerning various factors like storage space, type of storage unit, characteristics, and provide several research domains.

scholarly journals Construction of a century solar chromosphere data set for solar activity related research

2017 ◽  
Vol 3 (2) ◽  
pp. 5-8
Author(s):  
Линь Ганхуа ◽  
Lin Ganghua ◽  
Ван Сяо-Фань ◽  
Wang Xiao Fan ◽  
Ян Сяо ◽  
...  
Keyword(s):  
Data Mining ◽  
Solar Activity ◽  
Space Weather ◽  
Large Scale ◽  
Abnormal Behavior ◽  
Solar Chromosphere ◽  
Solar Cycles ◽  
Data Set ◽  
Related Research ◽  
Data Mining Algorithms

This article introduces our ongoing project “Construction of a Century Solar Chromosphere Data Set for Solar Activity Related Research”. Solar activities are the major sources of space weather that affects human lives. Some of the serious space weather consequences, for instance, include interruption of space communication and navigation, compromising the safety of astronauts and satellites, and damaging power grids. Therefore, the solar activity research has both scientific and social impacts. The major database is built up from digitized and standardized film data obtained by several observatories around the world and covers a timespan more than 100 years. After careful calibration, we will develop feature extraction and data mining tools and provide them together with the comprehensive database for the astronomical community. Our final goal is to address several physical issues: filament behavior in solar cycles, abnormal behavior of solar cycle 24, large-scale solar eruptions, and sympathetic remote brightenings. Significant progresses are expected in data mining algorithms and software development, which will benefit the scientific analysis and eventually advance our understanding of solar cycles.

scholarly journals Anomaly data management and big data analytics: an application on disability datasets

2018 ◽  
Vol 2 (2) ◽  
pp. 164-176
Author(s):  
Zhiwen Pan ◽  
Wen Ji ◽  
Yiqiang Chen ◽  
Lianjun Dai ◽  
Jun Zhang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Management ◽  
Big Data Analytics ◽  
Quality Data ◽  
Content Type ◽  
Data Mining Algorithms ◽  
Management Scheme ◽  
The Disabled ◽  
Mining Scheme

Purpose The disability datasets are the datasets that contain the information of disabled populations. By analyzing these datasets, professionals who work with disabled populations can have a better understanding of the inherent characteristics of the disabled populations, so that working plans and policies, which can effectively help the disabled populations, can be made accordingly. Design/methodology/approach In this paper, the authors proposed a big data management and analytic approach for disability datasets. Findings By using a set of data mining algorithms, the proposed approach can provide the following services. The data management scheme in the approach can improve the quality of disability data by estimating miss attribute values and detecting anomaly and low-quality data instances. The data mining scheme in the approach can explore useful patterns which reflect the correlation, association and interactional between the disability data attributes. Experiments based on real-world dataset are conducted at the end to prove the effectiveness of the approach. Originality/value The proposed approach can enable data-driven decision-making for professionals who work with disabled populations.

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