In this paper, a prediction algorithm using time series data mining based on fuzzy logic is proposed.
Earthquake prediction has been done from a synthetic earthquake time series by using investigating method at first step ago.
Time series has been transformed to phase space by using nonlinear time series analysis and then fuzzy logic has been used to prediction optimal values of important parameters characterizing the time series events.
Truth of prediction algorithm based fuzzy logic has been proved by application results.
In this thesis, some existing intelligent systems techniques such as Self-Organizing Map, Fuzzy C-means and decision tree are used to analyze several datasets.
The techniques are used to provide flexible information processing capability for handling real-life situations.
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)
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.