Data mining is the analysis step of the "Knowledge Discovery in Databases" process, or KDD. It is the process that results in the discovery of new patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract knowledge from an existing data set and tra...

The extraction of fuzzy association rules for the description of dependencies and interactions from large data sets as those arising in gene expression data analysis applications perplexes very difficult combinatorial problems that depend heavily on the size of these sets. The paper describes a two stage approach to the problem that obtains computationally manageable solutions. The first stage ...

This paper describes how we applied a fuzzy technique to a data-mining task involving a large database that was provided by an international bank with offices in Hong Kong. The database contains the demographic data of over 320,000 customers and their banking transactions, which were collected over a six-month period. By mining the database, the bank would like to be able to discover interestin...

A neural network trading strategy optimized based on Apriori and the genetic algorithm is proposed to determine strategies. Using Neural Network Genetic Algorithm, we find all frequent term sets of fuzzy set mine association rules from set. Enter price dataset, minimum support multiplier; output rule library, iterate.

For the sake of environmental change monitoring, a huge amount of geospatial and temporal data have been acquired through various networks of monitoring stations. For instance, daily precipitation and air temperature are observed at meteorological stations, and MODIS images are regularly received at satellite ground stations. However, so far these massive raw data from the stations are not full...

Fuzzy rules have been advocated as a key tool for expressing pieces of knowledge in "fuzzy logic". However, there does not exist a unique kind of fuzzy rules, nor is there only one type of "fuzzy logic". This diversity has caused many a misunderstanding in the literature of fuzzy control. The paper is a survey of different possible semantics for a fuzzy rule and shows how they can be captured i...

In the current data asset management risk assessment, processing of assessment indicators is relatively simple, which leads to large errors in results. To this end, a based on fuzzy hierarchy method and association rules proposed. Identify factors for management. Construct model. Quantify model method, generate judgment matrix derive comprehensive vector. Assess levels rules. Experiments show t...

This paper considers the automatic design of fuzzy rule-based classification systems from labeled data. The classification accuracy and interpretability of generated rules are of major importance in fuzzy classification systems. We propose a weighting function for compatibility grade of patterns that improves the performance of fuzzy classification system without degrading the interpretability ...