/**
 * <p>Generalized DBSCAN.</p>
 * 
 * Generalized DBSCAN is an abstraction of the original DBSCAN idea,
 * that allows the use of arbitrary "neighborhood" and "core point" predicates.
 * 
 * For each object, the neighborhood as defined by the "neighborhood" predicate
 * is retrieved - in original DBSCAN, this is the objects within an epsilon sphere
 * around the query object. Then the core point predicate is evaluated to decide if
 * the object is considered dense. If so, a cluster is started (or extended) to
 * include the neighbors as well.
 * 
 * <p>
 * Reference:<br />
 * Jörg Sander, Martin Ester, Hans-Peter Kriegel, Xiaowei Xu:<br />
 * Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its
 * Applications<br />
 * In: Data Mining and Knowledge Discovery, 1998.
 * </p>
 * 
 * @apiviz.exclude .*\.Instance$
 */
/*
 This file is part of ELKI:
 Environment for Developing KDD-Applications Supported by Index-Structures

 Copyright (C) 2015
 Ludwig-Maximilians-Universität München
 Lehr- und Forschungseinheit für Datenbanksysteme
 ELKI Development Team

 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU Affero General Public License as published by
 the Free Software Foundation, either version 3 of the License, or
 (at your option) any later version.

 This program is distributed in the hope that it will be useful,
 but WITHOUT ANY WARRANTY; without even the implied warranty of
 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 GNU Affero General Public License for more details.

 You should have received a copy of the GNU Affero General Public License
 along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */
package de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan;