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UNCERTAIN ONE-CLASS LEARNING AND CONCEPT SUMMARIZATION
LEARNING ON UNCERTAIN DATA STREAMS
ABSTRACT:
This paper presents a novel framework to uncertain one-class learning and concept
summarization learning on uncertain data streams. Our proposed framework consists of two
parts. First, we put forward uncertain one-class learning to cope with data of uncertainty.
We first propose a local kernel-density-based method to generate a bound score for each
instance, which refines the location of the corresponding instance, and then construct an
uncertain one-class classifier (UOCC) by incorporating the generated bound score into a oneclass SVM-based learning phase.
Second, we propose a support vectors (SVs)-based clustering technique to summarize the
concept of the user from the history chunks by representing the chunk data using support vectors
of the uncertain oneclass classifier developed on each chunk, and then extend k-mean clustering
method to cluster history chunks into clusters so that we can summarize concept from the history
chunks.

2. Our proposed framework explicitly addresses the problem of one-class learning and concept
summarization learning on uncertain one-class data streams. Extensive experiments on uncertain
data streams demonstrate that our proposed uncertain one-class learning method performs better
than others, and our concept summarization method can summarize the evolving interests of the
user from the history chunks.