Unsupervised Pattern Classification in Single and Multi-objective FrameworkSpeaker: Ujjwal Maulik – Kolkata, India
Topic(s): Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing
Data clustering is a popular unsupervised pattern classification technique that is used for partitioning a given data set into homogeneous groups based on some similarity/dissimilarity metric. There are several single objective clustering algorithms available and popular in the literature including KMean and FCM. Although both KMean and FCM are very popular algorithms, they may stuck to local optima depending on the choice of initial cluster centers. In this lecture first we will demonstrate how Metaheuristic techniques can be used to solve the problem of KMean/FCM. Result will be demonstrated for pixel classification of satellite images.
In the second part of the lecture we will discuss more on Multiobjective clustering, in which multiple objective functions are simultaneously optimized. Selecting one solution from the set of Pareto Optimal solutions is always a critical issue. We will also discuss how machine learning techniques like Support Vector Machine (SVM) can be used to combine the Pareto Optimal solutions to evolve a better solution. The result will be demonstrated for classification of Micro Array Gene Expression Data.
About this LectureNumber of Slides: 60
Duration: 120 minutes
Languages Available: English
Request this Lecture
To request this particular lecture, please complete this online form.
Request a Tour
To request a tour with this speaker, please complete this online form.
All requests will be sent to ACM headquarters for review.