Adaptive Methods for Classification in Arbitrarily Imbalanced and Drifting Data Streams

Author(s):  
Ryan N. Lichtenwalter ◽  
Nitesh V. Chawla
Author(s):  
LAKSHMI PRANEETHA

Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.


2020 ◽  
Vol 1 (12) ◽  
pp. 79-82
Author(s):  
M. U. USUPOV ◽  

The article deals with the application of adaptive methods of capital management at enterprises of the Toktogul district of the Kyrgyz Republic. This area of economic work is considered a key point in the functioning of the firm. Questions of formation and effective use of own and borrowed capital largely depend on the use of modern methods of analysis.


2012 ◽  
Vol 35 (3) ◽  
pp. 540-554 ◽  
Author(s):  
Shang-Lian PENG ◽  
Zhan-Huai LI ◽  
Qun CHEN ◽  
Qiang LI

Sign in / Sign up

Export Citation Format

Share Document