MOA 12.03
Real Time Analytics for Data Streams
Package moa.evaluation

Based on: "An Effective Evaluation Measure for Clustering on Evolving Data Streams" by Kremer H., Kranen P., Jansen T., Seidl T., Bifet A., Holmes G., Pfahringer B. More...

Classes

class  Accuracy
class  BasicClassificationPerformanceEvaluator
 Classification evaluator that performs basic incremental evaluation. More...
class  BasicClusteringPerformanceEvaluator
 Clustering evaluator that performs basic incremental evaluation. More...
class  BasicRegressionPerformanceEvaluator
 Regression evaluator that performs basic incremental evaluation. More...
interface  ClassificationMeasureCollection
 Classification Measure Collection interface that it is used to not appear in clustering. More...
interface  ClassificationPerformanceEvaluator
 Interface implemented by learner evaluators to monitor the results of the learning process. More...
class  CMM
class  CMM_GTAnalysis
class  EntropyCollection
class  EWMAClassificationPerformanceEvaluator
 Classification evaluator that updates evaluation results using an Exponential Weighted Moving Average. More...
class  F1
class  FadingFactorClassificationPerformanceEvaluator
 Classification evaluator that updates evaluation results using a fading factor. More...
class  General
class  LearningCurve
 Class that stores and keeps the history of evaluation measurements. More...
class  LearningEvaluation
 Class that stores an array of evaluation measurements. More...
interface  LearningPerformanceEvaluator
 Interface implemented by learner evaluators to monitor the results of the learning process. More...
class  MeasureCollection
class  MembershipMatrix
class  SilhouetteCoefficient
class  SSQ
class  StatisticalCollection
class  WindowClassificationPerformanceEvaluator
 Classification evaluator that updates evaluation results using a sliding window. More...
class  WindowRegressionPerformanceEvaluator
 Regression evaluator that updates evaluation results using a sliding window. More...

Detailed Description

Based on: "An Effective Evaluation Measure for Clustering on Evolving Data Streams" by Kremer H., Kranen P., Jansen T., Seidl T., Bifet A., Holmes G., Pfahringer B.

in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (2011), pp. 868-876

http://dl.acm.org/citation.cfm?id=2020555

This class calculates error values for the found clustering, based on the "ground truth analysis"

in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (2011), pp. 868-876

http://dl.acm.org/citation.cfm?id=2020555

This class contains the so called "ground truth analysis".

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