cblof

Cluster-based local outlier factor (CBLOF)

Citekey	HeEtAl2003Discovering
Source Code	https://github.com/yzhao062/pyod/blob/master/pyod/models/cblof.py
Learning type	unsupervised
Input dimensionality	multivariate

Parameters

• n_clusters: int, optional (default=8)
  The number of clusters to form as well as the number of centroids to generate.

• clustering_estimator: Estimator, optional (default=None)
  The base clustering algorithm for performing data clustering. A valid clustering algorithm should be passed in. The estimator should have standard sklearn APIs, fit() and predict(). The estimator should have attributes labels_ and cluster_centers_. If cluster_centers_ is not in the attributes once the model is fit, it is calculated as the mean of the samples in a cluster. If not set, CBLOF uses KMeans for scalability. See https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html. REMOVED default (KMeans) is used!

• alpha: float in (0.5, 1), optional (default=0.9)
  Coefficient for deciding small and large clusters. The ratio of the number of samples in large clusters to the number of samples in small clusters.

• beta: int or float in (1,), optional (default=5)
  Coefficient for deciding small and large clusters. For a list sorted clusters by size |C1|, \|C2|, ..., |Cn|, beta = |Ck|/|Ck-1|.

• use_weights: bool, optional (default=False)
  If set to True, the size of clusters are used as weights in outlier score calculation.

• check_estimator: bool, optional (default=False)
  If set to True, check whether the base estimator is consistent with sklearn standard. .. warning:: check_estimator may throw errors with scikit-learn 0.20 above. REMOVED must be False to work with new scikit-learn version!

• random_state: int, RandomState or None, optional (default=None)
  If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

• contamination: float in (0., 0.5), optional (default=0.1)
  The amount of contamination of the data set, i.e. the proportion of outliers in the data set. When fitting this is used to define the threshold on the decision function. Automatically determined by algorithm script!!

• n_jobs: int, optional (default = 1)
  The number of parallel jobs to run for neighbors search. If -1, then the number of jobs is set to the number of CPU cores. Affects only kneighbors and kneighbors_graph methods.

Citation format (for source code)

Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7.