Fuzzy Data Mining + Thesis

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This thesis also makes a comparison between the OWFDT and two benchmark algorithms, Fuzzy ID3 and weighted FDT.

SIx datasets ranging from material science to medical and civil engineering were introduced as case study applications.

This method creates a set of methods that reveal hidden temporal patterns that are characteristic and predictive of time series events.

Time series data mining examines the time series in a phase space.

A momentum term was also introduced into the learning process to train the weight connections to avoid oscillation or divergence.

A new reasoning mechanism has been also proposed to combine the constructed tree with those weights which had been optimized in the learning process.These datasets involve classification of composite material failure mechanism, classification of electrocorticography (ECo G)/Electroencephalogram (EEG) signals, eye bacteria prediction and wave overtopping prediction.Different intelligent systems techniques were used to cluster the patterns and predict the classes although OWFDT was used to design classifiers for all the datasets.This thesis first proposes an optimized weighted fuzzy decision tree, incorporating the introduction of Fuzzy C-Means to fuzzify the input instances but keeping the expected labels crisp.This leads to a different output layer activation function and weight connection in the neural network (NN) structure obtained by mapping the FDT to the NN. Fuzzy clustering of data mining: A survey paper, International Journal of Advance Research, Ideas and Innovations in Technology, International Journal of Advance Research, Ideas and Innovations in Technology, 5(3) OWFDT has also demonstrated its efficiency and effectiveness as compared with a conventional fuzzy Decision Tree and weighted fuzzy Decision Tree.The data mining on Web is difficult for online analytic processing (OLAP) with BIG DATA.Bootstrap aggregating (bagging) to Decision Tree also helped to select those most important sensors (features) so that the dimension of the data could be reduced.Those features which were most important were used to grow the OWFDT and the curse of dimensionality problem could be solved using this approach.

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