Abstract
Peano count trees (P-trees) provide efficient, lossless, data mining ready representations of tabular data and make possible the mining of multiple very large data sets, including time-sequences of Remotely Sensed Imagery (RSI) and micro-array gene expression datasets (MA). Each MA dataset presents a one-time, gene expression level map of thousands of genes subjected to hundreds of conditions. MA data has traditionally been archived as text abstracts (e.g., Medline abstracts). An important multimedia application is to integrate macro-scale analysis of RSI with the micro-scale analysis of MA across multiple plant organisms. This is truly a multimedia data mining problem. Most multimedia data is mined by extracting pertinent features into tables, then mining the tables. P-trees are a convenient technology to mine all such multimedia data.
Patents are pending on the P-tree technology. This work is partially supported by GSA Grant ACT# K96130308, NSF Grant OSR-9553368 and DARPA Grant DAAH04-96-1-0329.
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Perrizo, W., Jockheck, W., Perera, A., Ren, D., Wu, W., Zhang, Y. (2003). Multimedia Data Mining Using P-Trees. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds) Mining Multimedia and Complex Data. PAKDD 2002. Lecture Notes in Computer Science(), vol 2797. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39666-6_7
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DOI: https://doi.org/10.1007/978-3-540-39666-6_7
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