This paper studies the problem of categorical data clustering,
especially for transactional data characterized by high
dimensionality and large volume. Starting from a heuristic method
of increasing the height-to-width ratio of the cluster HISTOGRAM, we
develop a novel algorithm – CLOPE, which is very fast and
scalable, while being quite effective. We demonstrate the
performance of our algorithm on two real world
模式識別學(xué)習(xí)綜述.該論文的英文參考文獻(xiàn)為303篇.很有可讀價(jià)值.Abstract— Classical and recent results in statistical pattern
recognition and learning theory are reviewed in a two-class
pattern classification setting. This basic model best illustrates
intuition and analysis techniques while still containing the essential
features and serving as a prototype for many applications.
Topics discussed include nearest neighbor, kernel, and HISTOGRAM
methods, Vapnik–Chervonenkis theory, and neural networks. The
presentation and the large (thogh nonexhaustive) list of references
is geared to provide a useful overview of this field for both
specialists and nonspecialists.
北京大學(xué)ACM比賽題目
Write a program to read four lines of upper case (i.e., all CAPITAL LETTERS) text input (no more than 72 characters per line) from the input file and print a vertical HISTOGRAM that shows how many times each letter (but not blanks, digits, or punctuation) appears in the all-upper-case input. Format your output exactly as shown.
The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. The term image registration is most commonly used to denote the process of alignment of images , that is of transforming them to the common coordinate system. This is done by optimizing a similarity measure between the two images. A widely used measure is Mutual Information (MI). This method requires estimating joint HISTOGRAM of the two images. Experiments are presented that demonstrate the approach. The technique is intensity-based rather than feature-based. As a comparative assessment the performance based on normalized mutual information and cross correlation as metric have also been presented.
-The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. Mutual Information is the similarity measure used in this case for optimizing the two images. This method requires estimating joint HISTOGRAM of the two images. The fusion of images is the process of combining two or more images into a single image retaining important features from each. The Discrete Wavelet Transform (DWT) has become an attractive tool for fusing multimodal images. In this work it has been used to segment the features of the input images to produce a region map. Features of each region are calculated and a region based approach is used to fuse the images in the wavelet domain.
