The bionic DBMS is coming, but what will it look like?
Ryan Johnson, Ippokratis Pandis
CIDR 2013
In this paper, we will study the data stream clustering problem in the context of text and categorical data domains. While the clustering problem has been studied recently for numeric data streams, the problems of text and categorical data present different challenges because of the large and un-ordered nature of the corresponding attributes. Therefore, we will propose algorithms for text and categorical data stream clustering. We will propose a condensation based approach for stream clust and un-ordered nature of the corresponding attributes. Therefore, we will propose algorithms for text and categorical data stream clustering. We will propose a condensation based approach for stream clustering which summarizes the stream into a number of fine grained cluster droplets. These summarized droplets can be used in conjunction with a variety of user queries to construct the clusters for different input parameters. Thus, this provides an online analytical processing approach to stream clustering. We also study the problem of detecting noisy and outlier records in real time. We will test the approach for a number of real and synthetic data sets, and show the effectiveness of the method over the baseline OSKM algorithm for stream clustering. © 2009 Springer-Verlag London Limited.
Ryan Johnson, Ippokratis Pandis
CIDR 2013
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ACS Fall 2023
Vicki L Hanson, Edward H Lichtenstein
Cognitive Psychology
Merve Unuvar, Yurdaer Doganata, et al.
CLOUD 2014