Keynote: Fuzzy Pattern Recognition in Data Analysis

Data Analysis is a process to analyze data in terms of representing, describing, evaluating, interpreting the data using statistical methods. Data can come in the form of statistical representation or a vector of numbers in which numeric pattern recognition algorithms can deal with this type of data set. Another type of data can be in the form of syntactic data. For this type of data set, there is another research branch in pattern recognition called syntactic pattern recognition that is able to analyze it. Each sample in syntactic data set is normally represented as a string. The strings in the same data set can have different lengths. Also, the string does not have any mathematical meaning that we can calculated as if they are vectors of numbers.
One of the popular theories used in data analysis is Fuzzy set theory, an extension of the classical set introduced by Lotfi Zadeh in 1965. Since then, there are many theories and applications developed based on Fuzzy set theory. In this talk, there are three parts on the utilization of the Fuzzy pattern recognition in data analysis. First, we will show how to develop a fuzzy algorithm in a decision making when the data are a collection of fuzzy vectors (a vector of fuzzy numbers). Another is how to incorporate the Fuzzy set theory into a set of feature generation in the classification problem. The last part of the talk is how to incorporate the Fuzzy set theory into string grammar pattern recognition. All algorithms in this talk are developed at Computational Intelligence Research Laboratory, Chiang Mai University. In each part of the talk, we will show applications of these algorithms in several real-world problems, e.g., sign language translation system, face recognition, health applications and person identification.