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One of the most widely used time series classification is the 1-nearest neighbor (1-NN) classification algorithm which utilizes dynamic time warping (DTW) as a similarity measure. On large training data, though DTW is demonstrated to be highly accurate, its 1-NN classification typically takes significant amount of time to classify a given test sequence. The hotspot for this type of computation lies...
Dynamic time warping (DTW) distance measure has increasingly been used as a similarity measurement for various data mining tasks in place of traditional Euclidean distance metric due to its superiority in sequence-alignment flexibility. However, in some tasks where shape averaging is required, e.g., in template matching and k-means clustering problems, current averaging methods are inaccurate in that...
With ever more increases in both storage and computational power, mining data in real time has become more and more practical and rapidly consumed a large fraction of research works on data streams. Particularly for time series data, subsequence matching under dynamic time warping (DTW) distance has proven to work exceptionally well, but with higher time and space complexities, thus posing a much...
As time series mining has become more prevalent and attracted much research interest, recent goals and efforts have been shifted toward scalability issue. One of the successful solutions is finding suitable representation of the data via dimensionality reduction. In this work, we introduce a novel fractal representation for time series data, which uses merely three real values to represent any time...
With predominant communication technology in our digital world, music has turned digital, and much research has been shifted toward digital music processing. Singing voice separation is one of the active research areas since the singing voice itself contains abundant information within, such as melody, singer's characteristic, lyrics, language, emotion, etc. This wide variety of resources is very...
As music has turned digital, much research has been shifted toward digital music processing. Singing voice separation is one of the active research areas since the singing voice itself contains abundant information within, including melody, singerpsilas characteristic, lyrics, language, emotion, etc. These wide variety of resources are quite useful for music information retrieval (MIR), singer identification,...
After the generation of multimedia data turned digital, an explosion of interest in their data storage, retrieval, and processing has drastically increased. This includes videos, images, and audios, where we now have higher expectations in exploiting these data at hands. Typical manipulations are in some forms of video/image/audio processing, including automatic speech recognition, which require fairly...
After the generation of multimedia data turned digital, an explosion of interest in their data storage, retrieval, and processing has drastically increased. This includes videos, images, and audios, where we now have higher expectations in exploiting these data at hands. Typical manipulations are in some forms of video/image/audio processing, including automatic speech recognition, which require fairly...
The increasing interest in time series data mining has had surprisingly little impact on real world medical applications. Practitioners who work with time series on a daily basis rarely take advantage of the wealth of tools that the data mining community has made available. In this work, we attempt to address this problem by introducing a parameter-light tool that allows users to efficiently navigate...
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