Introduction

Granular Computing (GrC) is a general computation theory for effectively using granules such as subsets, neighborhoods, turples(ordered subsets), relations(subsets of Caresian products), fuzzy sets(membership functions), variables (measurable functions), Turing machines(algorithms),and intervals to build an efficient computational model for complex with huge amounts of data, information and knowledge. (See Granular Computing Informastion Center).

Though the label is relatively recent, the basic notions and principles are quite common, even ancient. For example, granulations of daily objects,such as human body, are ancient practices. The infinitesimal that led to invention of Calculus, are the ancient intuition on the granulatiion of space and time.

Heisenburg uncertainty (a neighborhood of possible locations)intergral part of modern physics (quantum mechanics). Information hiding in programming, divide and conquer in algoritnm development are common practices of granular computing in computer science.

Fuzzy and rough set theories, neutrosophic computing, quotient space, belief functions, machine learning, databases, data mining, cluster analysis, interval computing, more recently social computing, computing all involve granular computing.

 
Highlights

The highlight of this conference is that the conference will be held in Silicon Valley, California. Many high-tech companies, such as IBM, Google, and Yahoo etc and several distinguished universities (Stanford,UC Berkely and etc) are just around the corner. Some interactins may be organized.

The area now known as Silicon Valley has been a center of technological development since the 1950's. The name Silicon Valley from the early 1970's, when the area had become the center for many semiconductor companies. While still hosting semiconductor and microprocessor companies, the region now hosts the headquarters of tech companies of every kind, including many of the best known and most prestigious names in personal computers, Web search, Internet auctions, networking, storage, databases, etc.

 
Topics of Interest

The topics and areas include, but are not limited to:

Computational Intelligence 	Neural Networks, Fuzzy Systems, Evolutionary Computation, Rough Sets, etc.
Foundation of Data Mining and Learning Theory 	Probabilistic/Statistical Learning, Machine Learning, Kernel Machines, etc.
Foundation of Clouding computing and Social Computing 	Applications to Bioinformatics, Medical Informatics and Chemical Informatics e-Intelligence, Web Intelligence, Web Informatics, Web Mining and Semantic Web