Applications And Trends In Data Mining Pdf
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- Data Mining - Applications & Trends
- 5 Important Future Trends in Data Mining
- Data Mining Tutorial: What is | Process | Techniques & Examples
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Data Mining - Applications & Trends
Data mining is widely used in diverse areas. There are a number of commercial data mining system available today and yet there are many challenges in this field. In this tutorial, we will discuss the applications and the trend of data mining. The financial data in banking and financial industry is generally reliable and of high quality which facilitates systematic data analysis and data mining. Data Mining has its great application in Retail Industry because it collects large amount of data from on sales, customer purchasing history, goods transportation, consumption and services.
Data mining is one of the most widely used methods to extract data from different sources and organize them for better usage. In spite of having different commercial systems for data mining, a lot of challenges come up when they are actually implemented. With rapid evolution in the field of data mining, companies are expected to stay abreast with all the new developments. Complex algorithms form the basis for data mining as they allow for data segmentation to identify various trends and patterns, detect variations, and predict the probabilities of various events happening. The raw data may come in both analog and digital format, and is inherently based on the source of the data. Companies need to keep track of the latest data mining trends and stay updated to do well in the industry and overcome challenging competition. Businesses which have been slow in adopting the process of data mining are now catching up with the others.
5 Important Future Trends in Data Mining
Activities in data warehousing and mining are constantly emerging. Data mining methods, algorithms, online analytical processes, data mart and practical issues consistently evolve, providing a challenge for professionals in the field. Research and Trends in Data Mining Technologies and Applications focuses on the integration between the fields of data warehousing and data mining, with emphasis on the applicability to real-world problems. This book provides an international perspective, highlighting solutions to some of researchers' toughest challenges. Developments in the knowledge discovery process, data models, structures, and design serve as answers and solutions to these emerging challenges. This volume written by a notable collection of international researchers and scientists sets forward a sound presentation of the state-of-the-art in the field of data mining.
Data Mining Tutorial: What is | Process | Techniques & Examples
Data Mining is a process of finding potentially useful patterns from huge data sets. It is a multi-disciplinary skill that uses machine learning , statistics, and AI to extract information to evaluate future events probability. The insights derived from Data Mining are used for marketing, fraud detection, scientific discovery, etc. Data Mining is all about discovering hidden, unsuspected, and previously unknown yet valid relationships amongst the data.
Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning , statistics , and database systems. The term "data mining" is a misnomer , because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction mining of data itself. The book Data mining: Practical machine learning tools and techniques with Java  which covers mostly machine learning material was originally to be named just Practical machine learning , and the term data mining was only added for marketing reasons. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records cluster analysis , unusual records anomaly detection , and dependencies association rule mining , sequential pattern mining.
Data mining is a method of extracting data from multiple sources and organizing it to derive valuable insights.
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