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BibTeX @MISC{Wolff_1ageneric, author = {Ran Wolff and Kanishka Bhaduri and Hillol Kargupta and Senior Member}, title = {1A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems}, year = {}}
Mining Data Streams: A Review Mohamed Medhat Gaber, Arkady Zaslavsky and Shonali Krishnaswamy ... More details about distributed data mining could be found in [47]. Recently, the data generation rates in some ... Mining data stream techniques and systems are reviewed in sections 3 and 4 respectively. Open and
Data Stream Mining: A Review of Learning Methods and Frameworks Svitlana Volkova Center for Language and Speech Processing Johns Hopkins University [email protected] October 12, 2012 Abstract The goal of the paper is to review methods, algorithms and frameworks for processing and analyzing real time data streams.
A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems. A Publication, Kanishka Bhaduri's Collection - 7 years, 6 months ago Shared By: Kanishka Bhaduri
implementing distributed clustering algorithms is that it is possible that an algorithm will get stuck in local optima, never finding the optimal solution. Attempting to converge on an optimal solution can be even more difficult when data is distributed, where no single node is fully aware of all data points.
The field of Distributed Data Mining (DDM) deals with the problem of analyzing data by paying careful attention to the distributed computing, storage, communication, and human-factor related resources. ... A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems more. by Ran Wolff. ... A Local Facility Location Algorithm ...
Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are becoming increasingly popular where centralization of data is impossible for mining and analysis. Unfortunately, most of the existing data mining algorithms work only when data can be accessed in its entirety.
Context-Adaptive Big Data Stream Mining - Online Appendix Cem Tekin*, Member, IEEE, ... Abstract—Emerging stream mining applications require clas-sification of large data streams generated by single or multi-ple heterogeneous sources. Different classifiers can be used to ... learner distributed data mining systems where each learner has
such distributed data mining systems also come with signif-icant design challenges that are the focal point of this work. (1) Limited data access. In distributed data mining, each local learner has only limited access to the entire dataset [3]. There are two types of data partition [4]. In the instance-
A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems. Ran Wolff, Kanishka Bhaduri, Hillol Kargupta. April 2009 IEEE Transactions on Knowledge and Data Engineering: Volume 21 Issue 4, April 2009. Publisher: IEEE Educational Activities Department Bibliometrics:
1 A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems Ran Wolff, Kanishka Bhaduri, and Hillol Kargupta Senior Member, IEEE Abstract— In a large network of computers or wireless sensors, fact that the data is static or rapidly changing.
Ran Wolff, Kanishka Bhaduri, Hillol Kargupta: A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems. IEEE Trans. Knowl. Data Eng. 21(4): 465-478 (2009) 2008; 21 : Kanishka Bhaduri, Ran Wolff, Chris Giannella, Hillol Kargupta: Distributed Decision-Tree Induction in Peer-to-Peer Systems.
large distributed system generic local algorithm mining data stream global state large network dynamic scenario decision tree thorough experimental analysis wireless sensor information retrieval communication cost global data mining model step approach message routing efficient local algorithm data mining model k-means clustering wide class ...
Mobile Agent technology applied to the problem of mining data streams which, Mobile Agent application intelligence, distributed data stream mining to solve the noise present in the data processing is difficult to identify, classify slow, inefficient mining problems is proposed based on Mobile Agent distributed data stream mining model, the model design and data base displacement .
a generic local algorithm for mining data streams in large distributed systems ieee 2009 jdm52 a pure nash equilibrium-based game theoretical method for data replication across multiple servers ieee 2009 jdm53 histogram-based global load balancing in structured peer-to-peer systems ieee 2009 jdm54 multiscale representations for fast pattern
Monitoring data streams in a distributed system has attracted considerable interest in recent years. The task of feature selection (e.g., by monitoring the information gain of various features) requires a very high communication overhead when addressed using straightforward centralized algorithms. While most of the existing algorithms deal with monitoring simple aggregated values such as ...
This paper proposes a scalable, local privacy-preserving algorithm for distributed peer-to-peer (P2P) data aggrega - tion useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induc- tion, feature selection, and more.
A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems Article (PDF Available) in IEEE Transactions on Knowledge and Data Engineering 21(4):465 - 478 · .
(2009) A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems. IEEE Transactions on Knowledge and Data Engineering 21 :4, 465-478. (2009) Local Construction of Near-Optimal Power Spanners for Wireless Ad Hoc Networks.
R. Wolff, K. Bhaduri, H. Kargupta. A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems. IEEE Transactions on Knowledge and Data Engineering. Volume 21, Issue 4, pp. 465-478. April 2009. K. Bhaduri, H. Kargupta. A Scalable Local Algorithm for Distributed Multivariate Regression. Statistical Analysis and Data Mining ...
A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems Ran Wolff, Kanishka Bhaduri, and Hillol Kargupta Senior Member, IEEE Abstract—In a large network of computers or wireless sensors, each of the components (henceforth, peers) has some data about the global state of the system. Much of the system's functionality
mining data streams and proposes our algorithm output granularity approach. One-pass mining techniques using our approach are proposed in section 3. The empirical studies for clustering data streams using algorithm output granularity are shown and discussed in section 4. Section 5 presents related work in mining data streams algo-rithms.
Local algorithm or results also describe the specific formula (and results that are returned from the use of the formula) used by search engines in ranking businesses or websites. ... A generic local algorithm for mining data streams in large distributed systems, Wolff, R., Bhaduri, K., & Kargupta, H. (2009). IEEE Transactions on Knowledge and ...
Jan 03, 2013· GoSCAN: Decentralized scalable data clustering Clustering algorithms classically require access to the complete dataset. However, as huge amounts of data are increasingly originating from multiple, dispersed sources in distributed systems, alternative solutions are required.
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