Date of Publication :21st February 2018
Abstract: Data mining techniques can be applied to any type of old or new data, each of which can be best dealt with using specific technologies (not requiring all of them).In other words, data mining techniques are limited by data types, data set sizes, and task application environments. Each data set has its own suitable data mining solution. Data mining practitioners often face problems of the unavailability of all training data at the same time and the inability to process a large amount of data due to constraints such as lack of adequate system memory. Once older data mining techniques cannot be applied to new data types or if new data types cannot be converted to traditional data types, new data mining techniques will always need to be explored. The most popular and most basic form of data from the database, data warehouse, orderly data or sequence data, graphics data and text data. In other words, they are joint data, high-dimensional data, longitudinal data, streaming data, web data, numerical data, categorical data, or textual data.
Reference :
-
- Nan Li, Desheng Dash Wu. Using text mining and sentiment analysis for online forum hotpos detection and forecast,
- MichaelChau, Jennifer Xu. Mining communities and their relationship in blogs: A study of online hate groups. In: Int. J. Human-Computer Studies 65 (2007) 57-70.
- J. Han, M. Kamber and J. Pei. Data Mining: Concepts and Techniques., 3rd edition, Morgan Kaufmann, 2011. Yonezawa A. ABCL: An Object-Oriented Concurrent System. Cambridge: MIT Press, 1990.
- Cristian Danescu- Niculescu- Mizil, Yonezawa Gueorgi Kossinets, Jon Kleinberg, Lillian Lee. How Opinions are received by Online Communities: A Case Study on Amazon.com Helpfulness Votes.
- D. Sorokina, J. Gehrke, S. Warner, and P. Ginsparg. Plagiarism detection in arXiv.In Proc. ICDM, pages 1070- 1075, 2006.
- Pedro Domingos, Matt Richardson. Mining the Network Value of Customers