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    IJIT
    2004
    226views Artificial Intelligence» more  IJIT 2004»
    IMDC: An Image-Mapped Data Clustering Technique for Large Datasets
    14 years 5 months ago
    IMDC: An Image-Mapped Data Clustering Technique for Large Datasets
    Download  www.waset.org
    In this paper, we present a new algorithm for clustering data in large datasets using image processing approaches. First the dataset is mapped into a binary image plane. The synthe...
    Faruq A. Al-Omari, Nabeel I. Al-Fayoumi
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    FLAIRS
    2001
    104views Artificial Intelligence» more  FLAIRS 2001»
    Tracking Clusters in Evolving Data Sets
    14 years 5 months ago
    Tracking Clusters in Evolving Data Sets
    Download  www.ise.gmu.edu
    As organizations accumulate data over time, the problem of tracking how patterns evolve becomes important. In this paper, we present an algorithm to track the evolution of cluster...
    Daniel Barbará, Ping Chen
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    CLIN
    2003
    96views Computational Linguistics» more  CLIN 2003»
    Incremental Construction of Minimal Sequential Transducers
    14 years 5 months ago
    Incremental Construction of Minimal Sequential Transducers
    Download  www.cnts.ua.ac.be
    This paper presents an efficient algorithm for the incremental construction of a minimal acyclic sequential transducer (ST) from a list of input and output strings. The algorithm...
    Wojciech Skut
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    CCCG
    2003
    135views Computational Geometry» more  CCCG 2003»
    Computing the Closest Point to a Circle
    14 years 5 months ago
    Computing the Closest Point to a Circle
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    In this paper we consider the problem of computing the closest point to the boundary of a circle among a set S of n points. We present two algorithms to solve this problem. One al...
    Pinaki Mitra, Asish Mukhopadhyay, S. V. Rao
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    ICWN
    2004
    164views Wireless Networks» more  ICWN 2004»
    A Distributed Clustering Framework in Mobile Ad Hoc Networks
    14 years 5 months ago
    A Distributed Clustering Framework in Mobile Ad Hoc Networks
    Download  www.mohrahit.in
    Abstract-- In this paper, we present a completely distributed algorithm for partitioning a given set of mobile nodes into clusters. The proposed algorithm tries to reduce the amoun...
    Mohit Garg, R. K. Shyamasundar
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    ICONIP
    2004
    141views Information Technology» more  ICONIP 2004»
    Non-linear Dimensionality Reduction by Locally Linear Isomaps
    14 years 5 months ago
    Non-linear Dimensionality Reduction by Locally Linear Isomaps
    Download  ai.stanford.edu
    Algorithms for nonlinear dimensionality reduction (NLDR) find meaningful hidden low-dimensional structures in a high-dimensional space. Current algorithms for NLDR are Isomaps, Loc...
    Ashutosh Saxena, Abhinav Gupta, Amitabha Mukerjee
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    CAINE
    2003
    128views Computer Science» more  CAINE 2003»
    A Genetic Algorithm for Clustering on Very Large Data Sets
    14 years 5 months ago
    A Genetic Algorithm for Clustering on Very Large Data Sets
    Download  cs.hbg.psu.edu
    Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups h...
    Jim Gasvoda, Qin Ding
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    DAGSTUHL
    2001
    101views Software Engineering» more  DAGSTUHL 2001»
    Understanding Algorithms by Means of Visualized Path Testing
    14 years 5 months ago
    Understanding Algorithms by Means of Visualized Path Testing
    Download  www.cs.hut.fi
    Visualization of an algorithm offers only a rough picture of operations. Explanations are crucial for deeper understanding, because they help the viewer to associate the visualiza...
    Ari Korhonen, Erkki Sutinen, Jorma Tarhio
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    ICMLA
    2004
    83views Machine Learning» more  ICMLA 2004»
    Satisficing Q-learning: efficient learning in problems with dichotomous attributes
    14 years 5 months ago
    Satisficing Q-learning: efficient learning in problems with dichotomous attributes
    Download  faculty.cs.byu.edu
    In some environments, a learning agent must learn to balance competing objectives. For example, a Q-learner agent may need to learn which choices expose the agent to risk and whic...
    Michael A. Goodrich, Morgan Quigley
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    APPINF
    2003
    197views Information Technology» more  APPINF 2003»
    Fast Recursive Data Processing in Graphs using Reduction
    14 years 5 months ago
    Fast Recursive Data Processing in Graphs using Reduction
    Download  www.kbs.twi.tudelft.nl
    This paper presents an algorithm for recursive data processing in directed graphs. The proposed algorithm applies graph reduction in order to determine both starting points and a ...
    J. H. ter Bekke, J. A. Bakker
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