Fuzzy Data Mining + Thesis

Fuzzy Data Mining + Thesis-11
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.The data mining is made simple by approximating the databases of BIG DATA for knowledge discovery process particularly Map Reducing.

Fuzzy quantification is a subtopic of fuzzy logic which deals with the modelling of the quantified expressions we can find in natural language.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.In this paper we will give a general overview of the main applications of fuzzy quantifiers in this field as well as some ideas to use them in new application contexts. Data mining can be said to have the aim to analyze the observational datasets to find relationships and to present the data in ways that are both understandable and useful.In the material dataset, Self-Organizing Map and Fuzzy C-Means were used to cluster the acoustic event signals and classify those events to different failure mechanism, after the classification, OWFDT was introduced to design a classifier in an attempt to classify acoustic event signals.For the eye bacteria dataset, we use the bagging technique to improve the classification accuracy of Multilayer Perceptrons and Decision Trees.Abstract—Prediction of an event at a time series is quite important for engineering and economy problems.Time series data mining combines the fields of time series analysis and data mining techniques.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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