The multimedia data of the same semantics

             The problem of heterogeneous data
mining deals with the computational challenges of searching multimedia data in
a unified computational framework that can answer similarity queries of data
mining by accurate and efficient means. The advances in data collection
methodologies have generated large data-warehouses, in assortment of
application domains, including but not limited to, Internet applications for
multimedia retrieval and exchange. Heterogeneous data indexing has proven to be
a valuable tool for complex data mining in large data domains inherently
semi-structured in nature (Dua, S.,
Mannava, V.,2005). Multimedia Data Mining is the mining and analysis of various
types of data, including images, video, audio, and animation. The idea of
mining data which contains different kinds of information is the main objective
of multimedia data mining (Zaiane et. al., 1998). Because it is very common
that the heterogeneous multimedia data of the same semantics always exist
jointly in many domain and application specific databases, it is very helpful
to consider the location information when analyzing multimedia data (Yang,
Y., Zhuang,
Y., Wang,
W.,2008) .As multimedia
data mining incorporates the areas of text mining, as well as
hypertext/hypermedia mining, these fields are closely related. Much of the
information describing these other areas also apply to multimedia data mining.
Multimedia data mining is the mining of high-level multimedia information and
knowledge from large multimedia databases. A multimedia data mining system
prototype, MultiMediaMiner, has been designed and developed. It includes the
construction of a multimedia data cube which facilitates multiple dimensional
analysis of multimedia data, primarily based on visual content, and the mining
of multiple kinds of knowledge, including summarization, comparison,
classification, association, and clustering

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