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    » Anomaly Detection Using Process Mining
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    KDD
    2005
    ACM
    161views Data Mining» more  KDD 2005»
    Combining email models for false positive reduction
    14 years 8 months ago
    Combining email models for false positive reduction
    Download  www.cs.columbia.edu
    Machine learning and data mining can be effectively used to model, classify and discover interesting information for a wide variety of data including email. The Email Mining Toolk...
    Shlomo Hershkop, Salvatore J. Stolfo
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    COMAD
    2009
    175views Knowledge Management» more  COMAD 2009»
    Categorizing Concepts for Detecting Drifts in Stream
    13 years 8 months ago
    Categorizing Concepts for Detecting Drifts in Stream
    Download  www.cse.iitb.ac.in
    Mining evolving data streams for concept drifts has gained importance in applications like customer behavior analysis, network intrusion detection, credit card fraud detection. Se...
    Sharanjit Kaur, Vasudha Bhatnagar, Sameep Mehta, S...
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    KDD
    2004
    ACM
    207views Data Mining» more  KDD 2004»
    Belief state approaches to signaling alarms in surveillance systems
    14 years 8 months ago
    Belief state approaches to signaling alarms in surveillance systems
    Download  www.cs.cmu.edu
    Surveillance systems have long been used to monitor industrial processes and are becoming increasingly popular in public health and anti-terrorism applications. Most early detecti...
    Kaustav Das, Andrew W. Moore, Jeff G. Schneider
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    SDM
    2004
    SIAM
    253views Data Mining» more  SDM 2004»
    Density-Connected Subspace Clustering for High-Dimensional Data
    13 years 9 months ago
    Density-Connected Subspace Clustering for High-Dimensional Data
    Download  www.dbs.informatik.uni-muenchen.de
    Several application domains such as molecular biology and geography produce a tremendous amount of data which can no longer be managed without the help of efficient and effective ...
    Peer Kröger, Hans-Peter Kriegel, Karin Kailin...
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    JMLR
    2006
    132views more  JMLR 2006»
    Learning to Detect and Classify Malicious Executables in the Wild
    13 years 7 months ago
    Learning to Detect and Classify Malicious Executables in the Wild
    Download  jmlr.csail.mit.edu
    We describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. We gathered 1,971 benign and 1,651 malicious execu...
    Jeremy Z. Kolter, Marcus A. Maloof
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