Data mining
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Data mining involves using computational techniques to discover patterns in large data sets. It is part of the broader knowledge discovery in databases (KDD) process. The presentation discusses data mining tasks like classification, clustering, association rule mining, and summarization. It also covers the advantages of data mining for marketing, finance, manufacturing and governments, as well as the disadvantages of privacy and security issues. In conclusion, data mining can benefit businesses and society by discovering patterns in data, but privacy and security concerns must be addressed.
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Data anonymization is one of the solutions allowing companies to comply with the GDPR directive in terms of data protection. In this context, developers must follow several steps in the process of data anonymization in development and testing environments. Indeed, real personal and sensitive data must not leave the production environment which is very secure. Often, anonymization experts are faced with difficulties including the lack of data flows and mapping between data sources, the non-cooperation of the database project teams (refusal to change) or even the lack of skills of these teams present due to the age of the systems developed by experienced teams who unfortunately left the project. Other problems are lack of data models. The aim of this paper is to discuss an anonymization process of databases of banking applications and present our context-based recommendations to overcome the different issues met and the solutions to improve methodologies of data anonymization process.
Ib3514141422
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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INTRODUCTION TO DATA MINING
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This document provides an overview of a presentation on data governance and management. It begins with introductions and background about the presenter. The presentation aims to introduce key concepts of data governance and management, including rationale, goals, roles and responsibilities, and implementation methods. It provides an overview of the topics that will be covered in the data governance and management section of a data governance and ethics module, including data lifecycle stages, frameworks, policies, and maturity levels. It also discusses the importance of defining roles and responsibilities in a data governance operating model.
Data Mining
This document provides an introduction to data mining concepts including definitions, tasks, challenges, and techniques. It discusses data mining definitions, the data mining process including data preprocessing steps like cleaning, integration, transformation and reduction. It also covers common data mining tasks like classification, clustering, association rule mining and the Apriori algorithm. Overall, the document serves as a high-level overview of key data mining concepts and methods.
DATA SCIENCE METHODOLOGY FOR CYBERSECURITY PROJECTS
Cybersecurity solutions are traditionally static and signature-based. The traditional solutions
along with the use of analytic models, machine learning and big data could be improved by
automatically trigger mitigation or provide relevant awareness to control or limit consequences
of threats. This kind of intelligent solutions is covered in the context of Data Science for
Cybersecurity. Data Science provides a significant role in cybersecurity by utilising the power
of data (and big data), high-performance computing and data mining (and machine learning) to
protect users against cybercrimes. For this purpose, a successful data science project requires
an effective methodology to cover all issues and provide adequate resources. In this paper, we
are introducing popular data science methodologies and will compare them in accordance with
cybersecurity challenges. A comparison discussion has also delivered to explain methodologies’
strengths and weaknesses in case of cybersecurity projects.
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Data mining
- 1. PRESENTATION ON DATA MINING MONSUR AHMED SHAFIQ ID:151-15-5439 SEC: G
- 2. Data Mining Data mining is the computational process of discovering patterns in large data sets. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD
- 3. Knowledge Discovery in Databases The term Knowledge Discovery in Databases refers to the broad process of finding knowledge in data.
- 4. Data mining task 1. Classification Classification maps data into predefined groups or classes. Represented by methods such as decision trees, etc. 2. Clustering Clustering is similar to classification .Except that the groups are not predefined. 3. Association Rule Association refers to uncovering relationship among data. 4. Summarization Summarization of general characteristics or features of target class of data.
- 5. Advantages and Disadvantages of Data Mining Advantages : 1.Marketing 2.Finance 3.Manufacturing 4.Governments Disadvantages: 1.Privacy Issues 2.Security issues
- 6. Conclusion Discovering interesting patterns from large amounts of data. Data mining brings a lot of benefits to businesses, society, governments as well as the individual. However, privacy, security, and misuse of information are the big problems if they are not addressed and resolved properly.