a Bipartite graph and is constructed based on the spatial and/or non-spatial attributes of the spatial objects in the dataset. Secondly, RW techniques are utilized on the graphs to compute the outlierness for each point (the differences between spatial objects and their spatial neighbours). The top k objects with higher outlierness are recognized as outliers.

Author | Sigal Shaked & Ben Nasi |

Date of publication | 2014-06-24 23:30:50 |

Maintainer | Sigal Shaked <shaksi@post.bgu.ac.il> |

License | GPL (>= 2) |

Version | 1.0 |

RWBP

RWBP/NAMESPACE

RWBP/R

RWBP/R/RW-BP-internal.R
RWBP/R/RWBP.R
RWBP/R/plot.RWBP.R
RWBP/R/RWBP.default.R
RWBP/R/RWBP.formula.R
RWBP/R/predict.RWBP.R
RWBP/R/print.RWBP.R
RWBP/MD5

RWBP/DESCRIPTION

RWBP/man

RWBP/man/RWBP-package.Rd
RWBP/man/predict.RWBP.Rd
RWBP/man/RWBP.Rd
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.