class_docstr: Detector that detects anomaly based on clustering of historical data.
  
  This detector peforms clustering using a clustering model, and identifies
  a time points as anomalous if it belongs to the minimal cluster.
  
  Parameters
  ----------
  model: object
      A clustering model to be used for clustering time series values. Same
      as a clustering model in scikit-learn, the model should minimally have
      a `fit` method and a `predict` method. The `predict` method should
      return an array of cluster labels.
class_docstr_tok: ["Detector","that","detects","anomaly","based","on","clustering","of","historical","data",".","This","detector","peforms","clustering","using","a","clustering","model",",","and","identifies","a","time","points","as","anomalous","if","it","belongs","to","the","minimal","cluster",".","Parameters","--","--","--","--","--","model",":","object","A","clustering","model","to","be","used","for","clustering","time","series","values",".","Same","as","a","clustering","model","in","scikit-learn",",","the","model","should","minimally","have","a","`","fit","`","method","and","a","`","predict","`","method",".","The","`","predict","`","method","should","return","an","array","of","cluster","labels","."]
code: def _param_names(self) -> Tuple[str, ...]:
      return ("model",)
  
imports: from collections import Counter
  from typing import Any, Callable, Dict, Optional, Tuple
  import pandas
  from .._detector_base import _TrainableMultivariateDetector
  from ..aggregator import AndAggregator
  from ..detector import InterQuartileRangeAD, ThresholdAD
  from ..pipe import Pipeline, Pipenet
  from ..transformer import CustomizedTransformer1D, PcaReconstructionError, RegressionResidual
label_desc: Use image node_id 2 for calling the MinClusterDetector obj's underlying member method code with example usage: obj._param_names() and returns: str