Advanced Data Mining with Weka

The University of Waikato

(A full index to the course appears at the end of Week 1.)

Topic		Step
Datasets	Forest cover type (covtypeNorm)	2.3, 2.9, 2.12
	labelled_tweets	2.12
	NSW Electricity Market (elecNormNew)	2.5, 2.12
	Signal peptide cleavage data (sigdata)	2.13, 2.14
Classifiers	HoeffdingAdaptiveTree	2.8, 2.9
	HoeffdingOptionTree	2.5
	HoeffdingTree (Weka)	2.2, 2.3, 2.5
	HoeffdingTree (MOA)	2.5, 2.6, 2.7, 2.9, 2.10
	IBk	2.3
	Incremental	2.2, 2.3
	J48	2.3, 2.13, 2.14
	MajorityClass	2.9, 2.12
	NaiveBayes	2.7, 2.9
	NaiveBayesUpdateable	2.3
	NaiveBayesMultinomial	2.10, 2.12
	SGD (Stochastic gradient descent)	2.10
	SGDText	2.12
	Updateable classifiers	2.2
Metalearners	LeveragingBag	2.8, 2.9
	OzaBag	2.4, 2.5, 2.7, 2.9
	OzaBagAdwin	2.8, 2.9
Filters	Resample	2.5
	StringToWordVector	2.12
Packages	massiveOnlineAnalysis (MOA)	2.4, 2.5
Generators	HyperplaneGenerator	2.5, 2.6
	LED24 (Weka)	2.3
	LEDGenerator (MOA)	2.4
	RandomRBFGenerator	2.8, 2.9
	RandomRBFGeneratorDrift	2.9
	WaveformGenerator	2.7
Evaluation	Kappa statistic	2.7, 2.10, 2.12
	Kappa-temporal statistic	2.12
	Periodic holdout	2.6, 2.7
	Prequential	2.6, 2.7, 2.9, 2.10, 2.12
Plus …	Adaptive sliding window (ADWIN)	2.8
	Bioinformatics	2.13, 2.14
	Bootstrap sampling	2.8
	Data stream mining	2.1–2.12
	MOA system	2.6–2.12
	Sequence analysis	2.13, 2.14
	Sentiment analysis	2.10, 2.11, 2.12
	Signal peptide prediction	2.13, 2.14
	Twitter	2.10, 2.11, 2.12

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The University of Waikato

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