Kargupta, H., Hamzaoglu, I., Stafford, B., Hanagandi, V., and Buescher, K., (1997), "PADMA: Parallel Data Mining Agents For Scalable Text Classification", Proceedings Of The High Performance Computing Conference, The Society For Computer Simulation International, pp. 290-295.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....exploit the main memory of all the processors. Parallel data mining algorithms have been recently considered for tasks such as association rules and classification, see, for example, Agrawal and Shafer1996] Chattratichat et al..1997] Cheung and Xiao1999] Han et al..1997] Joshi et al..1998] [Kargupta et al..1997], Shafer et al..1996] Srivastava et al..1998] Zaki et al..1998] and [Zaki et al..1997] Also, see [Stolorz and Musick1997] and [Freitas and Lavington1998] for recent books on scalable and parallel data mining. In this paper, we consider parallel clustering. Clustering or grouping of similar ....

H. Kargupta, I. Hamzaoglu, B. Sta#ord, V. Hanagandi, and K. Buescher. PADMA: Parallel data mining agents for scalable text classification. In Proceedings of the High Performance Computing, Atlanta, GA, USA, pages 290-- 295, 1997.

.... algorithms have been recently considered for tasks such as association rules and classi cation, see, for example, Agrawal and Shafer [1] Chattratichat et al. 2] Cheung and Xiao [3] Han, Karypis, and Kumar [4] 2 Dhillon Modha Joshi, Karypis, and Kumar [5] Kargupta, Hamzaoglu, and Sta ord [6], Shafer, Agrawal, and Mehta [7] Srivastava, et al. 8] Zaki, Ho, and Agrawal [9] and Zaki et al. 10] Also, see Stolorz and Musick [11] and Freitas and Lavington [12] for recent books on scalable and parallel data mining. In this paper, we consider parallel clustering. Clustering or grouping ....

Kargupta, H., Hamzaoglu, I., Staord, B., Hanagandi, V., Buescher, K.: PADMA: Parallel data mining agents for scalable text classication. In: Proceedings of the High Performance Computing, Atlanta, GA, USA. (1997) 290-295

.... data mining algorithms have been recently considered for tasks such as association rules and classification, see, for example, Agrawal and Shafer [1] Chattratichat et al. 6] Cheung and Xiao [8] Han, Karypis, and Kumar [22] Joshi, Karypis, and Kumar [24] Kargupta, Hamzaoglu, and Stafford [25], Shafer, Agrawal, and Mehta [32] Srivastava, et al. 38] Zaki, Ho, and Agrawal [41] and Zaki et al. 42] Also, see Stolorz and Musick [39] and Freitas and Lavington [17] for recent books on scalable and parallel data mining. In this paper, we consider parallel clustering. Clustering or ....

H. Kargupta, I. Hamzaoglu, B. Stafford, V. Hanagandi, and K. Buescher. PADMA: Parallel data mining agents for scalable text classification. In Proceedings of the High Performance Computing, Atlanta, GA, USA, pages 290--295, 1997.

....source. These rules are then ranked using some criterion and some number of the top ranked rules are selected to form the rule set. In [26] the authors report a technique to automatically produce a Bayesian belief network from discovered knowledge using a distributed approach. The PADMA system [19, 18] also deals with the problem of distributed data mining from homogeneous data sites. This system implemented a distributed clustering algorithm that was aided by relevance feedbackbased supervised learning techniques. The PADMA system has an agent based distributed architecture where the partial ....

H. Kargupta, I. Hamzaoglu, B. Staord, V. Hanagandi, and K. Buescher. PADMA: Parallel data mining agent for scalable text classication. In Proceedings Conference on High Performance Computing '97, pages 290-295. The Society for Computer Simulation International, 1996.

....In [7] the author shows an adaptation of the SLINK [2] and other agglomerative hierarchical clustering algorithms to a multiprocessor environment to parallelize the clustering process. In [8] the authors adapt the K Means algorithm to run in a parallel environment. The PADMA system [9, 10] achieves scalability by locating agents with the distributed data sources. An agent coordinating facilitator gives user requests to local agents that then access and analyze local data, returning analysis results to the facilitator, which merges the results. The high level results returned by the ....

Kargupta, H., Hamzaoglu, I., Stafford, B., Hanagandi, V., Buescher, K.: PADMA: Parallel data mining agent for scalable text classification. In: Proceedings Conference on High Performance Computing '97, The Society for Computer Simulation International (1996) 290--295

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H. Kargupta, I. Hamzaoglu, B. Stafford, V. Hanagandi, and K. Buescher, "PADMA: PArallel Data Mining Agents For Scalable Text Classification", High Performance Computing Conference, pp. 290-295, April 1997.

....in a hierarchy of meta classifiers. A number of different learning algorithms are available through this system. Another approach to multi agent based distributed machine learning is described in (Provost Hennessy, 1996; Provost Aronis, 1996; Provost Venkateswarlu, 1998) The PADMA system (Kargupta, Hamzaoglu, Stafford, Hanagandi, Buescher, 1996; Kargupta, Hamzaoglu, Stafford, 1997) achieves scalability by locating agents with the distributed data sources. An agent coordinating facilitator gives user requests to local agents which then access and analyze local data, returning analysis results to the facilitator, which merges the ....

Kargupta, H., Hamzaoglu, I., Stafford, B., Hanagandi, V., & Buescher, K. (1996). PADMA: Parallel data mining agent for scalable text classification. In Proceedings Conference on High Performance Computing '97 (pp. 290--295). The Society for Computer Simulation International.

....distribution. One model applies to situations in which the agent learners observe data sequences generated according to the identical target distribution, while the second model applies when the data sequences may not have the identical target distribution over all agent learners. The PADMA system (Kargupta, Hamzaoglu, Stafford, Hanagandi, Buescher, 1996; Kargupta, Hamzaoglu, Stafford, 1997) achieves scalability by locating agents with the distributed data sources. An agent coordinating facilitator gives user requests to local agents that then access and analyze local data, returning analysis results to the facilitator, which merges the ....

Kargupta, H., Hamzaoglu, I., Stafford, B., Hanagandi, V., & Buescher, K. (1996). PADMA: Parallel data mining agent for scalable text classification. In Proceedings Conference on High Performance Computing '97 (pp. 290--295). The Society for Computer Simulation International.

....the power of distributed data mining (DDM) The field of distributed data mining deals with the KDD problem in a distributed environment. DDM is getting increasing attention and a growing body of work is becoming available. The meta learning (Chan Stolfo, 1993) based JAM system, the PADMA system (Kargupta et al. 1996), the WoRLD system (Aronis, Kolluri, Provost, Buchanan, 1996) the BODHI system (Kargupta et al. 1998) are some examples of DDM systems. Additional DDM related work may be found elsewhere (Lam Segre, 1997; Nowak, 1998) A typical application domain of DDM either has inherently distributed ....

Kargupta, H., Hamzaoglu, I., Stafford, B., Hanagandi, V., & Buescher, K. (1996). PADMA: Parallel data mining agent for scalable text classification.

....These include table creation and deletion, hash index creation and deletion, parallel select and join operations. PADMA achieves parallel query processing through intra operator parallelism. Detailed description about the specific implementation of each of these operations can be found elsewhere (Kargupta, Hamzaoglu, Stafford, 1996). Parallel Data Analysis In PADMA, data analysis is primarily done by the agents in a distributed fashion. Every agent returns a concept graph (which may be either a hierarchical graph of clusters, or decision trees, or statistical analysis results such as correlation matrix) to the ....

....susceptible to spelling errors. PADMA text DMAs use an n gram representation (Damenshek, 1995) of texts at the bottom level for rough analysis. A combination of different statistical representations and linear classifiers are used to generate a hierarchical representation of text documents (Kargupta, Hamzaoglu, Stafford, 1996). Detailed description of the distributed implementation of this algorithm can be found elsewhere (Kargupta, Hamzaoglu, Stafford, 1996) Numeric DMAs PADMA currently contains data mining agents for numeric data analysis, which are still in the developmental process. Current numeric DMAs ....

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Kargupta, H., Hamzaoglu, I., & Stafford, B. (1996, October). PADMA: PArallel Data Mining Agents for scalable text classification. Los Alamos National Laboratory Unclassified Report LAUR-96-3491. To be published in the proceedings of High Performance Computing '97.

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Kargupta, H., Hamzaoglu, I., Stafford, B., Hanagandi, V., and Buescher, K., (1997), "PADMA: Parallel Data Mining Agents For Scalable Text Classification", Proceedings Of The High Performance Computing Conference, The Society For Computer Simulation International, pp. 290-295.

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