Atmospheric pattern recognition of human activities on ubiquitous sensor network using data stream mining algorithms

2016 ◽  
Vol 20 (3) ◽  
pp. 147 ◽  
Author(s):  
Hang Yang ◽  
Simon Fong ◽  
Kyungeun Cho ◽  
Junbo Wang
Keyword(s):  
Pattern Recognition ◽  
Sensor Network ◽  
Human Activities ◽  
Data Stream ◽  
Data Stream Mining ◽  
Stream Mining ◽  
Using Data ◽  
Mining Algorithms

Research on the Mining Technology of Multimedia Communication Data Flow Based on Computer Network

2013 ◽  
Vol 380-384 ◽  
pp. 3570-3574
Author(s):  
Yong Hong Cheng ◽  
Li Hua Ouyang ◽  
Xin Yan Liu
Keyword(s):  
Data Stream ◽  
High Speed ◽  
Data Flow ◽  
Computer Network ◽  
Multimedia Communication ◽  
Data Stream Mining ◽  
Stream Mining ◽  
Mining Technology ◽  
Daily Work ◽  
Mining Algorithms

computer network multimedia communication has spread all over daily work and life fields, people also have put forward higher requirements for computer multimedia communication network. Based on current network multimedia communication, there are a large amount of data transmission, high-speed and dynamic characteristics, the mining technology of its communication data flow is carried out related to research. In this paper, the frequent item sets of data stream mining technology is carried out related to research, and the classical HCOUNT algorithm is carried out relevant analysis, according to the relevant analysis, the classical HCOUNT algorithm is improved, in order to alleviate possible error problem in data mining. Finally, the data stream mining algorithms are carried out relevant analysis of actual experimental data. Study on the technical aspects of the data flow mining in computer network multimedia communication, it is an important role in the development of the computer network multimedia communication. Study on the improvement of the current algorithm, it provides reference in the similar field of algorithm research

scholarly journals Anomalous Network Packet Detection Using Data Stream Mining

2011 ◽  
Vol 02 (04) ◽  
pp. 158-168 ◽  
Author(s):  
Zachary Miller ◽  
William Deitrick ◽  
Wei Hu
Keyword(s):  
Data Stream ◽  
Data Stream Mining ◽  
Stream Mining ◽  
Using Data ◽  
Packet Detection

Knowledge Discovery Using Data Stream Mining

2018 ◽  
pp. 231-258
Author(s):  
Prasanna Lakshmi Kompalli
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Relevant Information ◽  
Research Community ◽  
Data Stream Mining ◽  
Data Sets ◽  
Stream Mining ◽  
Real World Problem ◽  
Using Data ◽  
Over Time

In recent years, advancement in technologies has made it possible for most of the present-day organizations to store and record large streams of data. Such data sets, which continuously and rapidly grow over time, are referred to as data streams. Mining of such data streams is a unique opportunity and also a challenging task. Data stream mining is a process of gaining knowledge from continuous and rapid records of data. Due to increased streaming information, data stream mining has attracted the research community in the recent past. There is voluminous literature that has been published in this domain over the past few years. Due to this, isolating the correct study would be grueling task for researchers and practitioners. While addressing a real-world problem, it would be difficult to find relevant information as it would be hidden in data streams. This chapter tries to provide solution as it is an amalgamation of all techniques used for data stream mining.

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