Archive for the ‘Fuzzy Logic’ Category

A survey of fuzzy web mining

Thursday, April 18th, 2013

A survey of fuzzy web mining by Chun-Wei Lin and Tzung-Pei Hong. (Lin, C.-W. and Hong, T.-P. (2013), A survey of fuzzy web mining. WIREs Data Mining Knowl Discov, 3: 190–199. doi: 10.1002/widm.1091)

Abstract:

The Internet has become an unlimited resource of knowledge, and is thus widely used in many applications. Web mining plays an important role in discovering such knowledge. This mining can be roughly divided into three categories, including Web usage mining, Web content mining, and Web structure mining. Data and knowledge on the Web may, however, consist of imprecise, incomplete, and uncertain data. Because fuzzy-set theory is often used to handle such data, several fuzzy Web-mining techniques have been proposed to reveal fuzzy and linguistic knowledge. This paper reviews these techniques according to the three Web-mining categories above—fuzzy Web usage mining, fuzzy Web content mining, and fuzzy Web structure mining. Some representative approaches in each category are introduced and compared.

Written to cover fuzzy web mining but generally useful for data mining and organization as well.

Fuzzy techniques are probably closer to our mental processes than the precision of description logic.

Being mindful that mathematical and logical proofs are justifications for conclusions we already hold.

They are not the paths by which we arrived at those conclusions.

Fuzzy machine learning framework v1.2

Saturday, June 2nd, 2012

Fuzzy machine learning framework v1.2

From the announcement:

The software is a library as well as a GTK GUI front-end for machine learning projects. Features:

  • Based on intuitionistic fuzzy sets and the possibility theory;
  • Features are fuzzy;
  • Fuzzy classes, which may intersect and can be treated as features;
  • Numeric, enumeration features and ones based on linguistic variables;
  • Derived and evaluated features;
  • Classifiers as features for building hierarchical systems;
  • User-defined features;
  • An automatic classification refinement in case of dependent features;
  • Incremental learning;
  • Object-oriented software design;
  • Features, training sets and classifiers are extensible objects;
  • Automatic garbage collection;
  • Generic data base support (through ODBC);
  • Text I/O and HTML routines for features, training sets and classifiers;
  • GTK+ widgets for features, training sets and classifiers;
  • Examples of use.

This release is packaged for Windows, Fedora (yum) and Debian (apt). The software is public domain (licensed under GM GPL).

http://www.dmitry-kazakov.de/ada/fuzzy_ml.htm

Unless you have time to waste, I would skip the religious discussion about licensing options.

For IP issues, hire lawyers, not programmers.

North American Fuzzy Information Processing Society (NAFIPS)

Wednesday, October 19th, 2011

North American Fuzzy Information Processing Society (NAFIPS)

From the website:

As the premier fuzzy society in North America established in 1981, our purpose is to help guide and encourage the development of fuzzy sets and related technologies for the benefit of mankind. In this role, we understand the importance of, and the need for, developing a strong intellectual basis and encouraging new and innovative applications. In addition, we acknowledge our leadership role to foster interaction and technology transfer to other national and international organizations to bring the benefits of this technology to North America and the world.

Links, pointers to software, journals, etc.

NAFIPS 2012 : North American Fuzzy Information Processing Society

Wednesday, October 19th, 2011

NAFIPS 2012 : North American Fuzzy Information Processing Society

Dates:

When Aug 6, 2012 – Aug 8, 2012
Where Berkeley, CA
Submission Deadline Jan 29, 2012
Notification Due Mar 11, 2012
Final Version Due Apr 15, 2012

From the announcement:

Aims and Scope

NAFIPS 2012 aims to bring together researchers, engineers and practitioners to present the latest achievements and innovations in the area of fuzzy information processing, to discuss thought-provoking developments and challenges, to consider potential future directions.

Topics

The topics cover all aspects of fuzzy systems and their applications including, but not limited to:

  • fuzzy sets and fuzzy logic
  • mathematical foundations of fuzzy sets and fuzzy systems
  • approximate reasoning, fuzzy inference models, and soft computing
  • fuzzy decision analysis, decision making, optimization, and design
  • fuzzy system architectures and hardware
  • fuzzy methods in data analysis, statistics and imprecise probability
  • fuzzy databases and information retrieval
  • fuzzy pattern recognition and image processing
  • fuzzy sets in management science
  • fuzzy control and robotics
  • possibility theory
  • fuzzy sets and logic in ontology, web, and social networks
  • fuzzy preference modelling
  • fuzzy sets in operations research and manufacturing
  • fuzzy database mining and financial forecasting
  • fuzzy neural networks
  • evolutionary and hybrid systems
  • intelligent agents and ambient intelligence
  • learning, adaptive, and evolvable fuzzy systems

Fuzzy Logic – Tutorial

Saturday, December 18th, 2010

Fuzzy Logic Author: Michael Berthold, Department of Computer and Information Science, University of Konstanz

Description:

The tutorial will introduce the basics of fuzzy logic for data analysis. Fuzzy Logic can be used to model and deal with imprecise information, such as inexact measurements or available expert knowledge in the form of verbal descriptions. We will first introduce the concepts of fuzzy sets, degrees of membership and fuzzy set operators. After discussions on fuzzy numbers and arithmetic operations using them, the focus will shift to fuzzy rules and how such systems of rules can be derived from available data.

Subject identity isn’t always crisp. Fuzzy logic offers a way to deal with those situations.

Context-aware intelligent recommender system

Tuesday, October 5th, 2010

Context-aware intelligent recommender system Authors: Mehdi Elahi Keywords: active learning, classification, context-aware, fuzzy logic, recommendation systems, recommenders

Abstract:

This demo paper presents a context-aware recommendation system. The system mines data from user’s web searches and other sources to improve the presentation of content on visited web pages. While user is browsing the internet, a memory resident agent records and analyzes the content of the webpages that were either searched for or visited in order to identify topic preferences. Then, based on such information, the content of requested web page is ranked and classified with different styles. The demo shows how a music weblog can be modified automatically based on user’s affinities.

Context-aware recommendation systems help present relevant information in large topic maps but I am more interested in their use for authoring systems.

Automatic construction of topics/roles/associations based on prior choices (for user approval) comes to mind.

Not a tool for a casual author but certainly a power tool for professional information explorers. (librarians?)