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We shall assume that we are mining a database, that data arrives in a stream or streams, and if it is not processed immediately or stored, then it is lost forever. moreover, we shall assume that the data arrives so rapidly that it is not feasible to store it all in active storage and then interact with it at
As this chapter focuses on the mining of stream data, time-series data, and sequence data, lets look at each of these areas. imagine a satellite-mounted remote sensor that is constantly generating data. the innite. this is an example of stream data. other examples include telecommu-
data mining: principles and algorithms chapter mining stream, time-series, and sequence data mining data streams mining time-series data mining sequence patterns in transactional databases mining sequence patterns in biological data data mining: principles and algorithms mining sequence patterns in biological data
In this paper, we discuss the research challenges in science and engineering, from the data mining perspective, with a focus on the following issues: information network analysis, discovery, usage, and understanding of patterns and knowledge, stream data mining, mining moving object data, rfid data, and data from sensor networks
chapter trends and research frontiers in data mining 13.1 bibliographic notes for mining complex types of data, there are many research papers and books stream data mining research covers stream cube model, e.g chen, dong, han, et al. cd, stream frequent pattern mining, e.g manku and motwani and karp, pa-padimitriou and
data mining: data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. forecasting forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends.
In particular, it addresses the domain of streaming data, which has recently become one of the emerging topics in data science, big data, and related areas. existing titles do not provide sufficient information on this topic. sample chapter chapter streaming data mining with massive online analytics contents:
4)data transformation ----------5) data mining pattern evaluation
chapter introduction to data mining: By osmar zaiane: printable versions: in pdf and in postscript We are in an age often referred to as the information age. In this information age, because we believe that information leads to power and success, and thanks to sophisticated technologies such as computers, satellites, etc we have been collecting tremendous amounts of information.
warehouse, transactional data, stream, spatiotemporal, time -series, sequence, text and web, multi -media, graphs & social and information networks. multi-dimensional view of data mining chapter introduction why data mining?
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