Fuzzy Clustering and Knowledge Discovery in Databases

Clustering and Knowledge Discovery in Spatial Databases

In the past decades, clustering has been widely used in areas such as pattern recognition, data analysis, and image processing. Recently, clustering has been recognized as a useful method for knowledge discovery in spatial databases. To eeciently detect clusters from large spatial databases with limited amount of available memory, special database techniques have been developed. In this article...

Clustering-regression-ordering steps for knowledge discovery in spatial databases

Precision agriculture is a new approach to farming in which environmental characteristics at a sub-field level are used to guide crop production decisions. Instead of applying management actions and production inputs uniformly across entire fields, they are varied to match site-specific needs. A first step in this process is to define spatial regions having similar characteristics and to build ...

ITERATE: A Conceptual Clustering Method for Knowledge Discovery in Databases

As the eld of Arti cial Intelligence (AI) matures, researchers are turning more and more to real world applications. With the widespread use of computers, it is estimated that the amount of information collected in the world doubles every 20 months. A substantial amount of this data comes from studying the operations of complex engineering systems (e.g., manufacturing lines, nuclear plants), ge...

Knowledge Discovery in Fuzzy Databases Using Attribute-Oriented Induction

In this paper we analyze an attribute-oriented data induction technique for discovery of generalized knowledge from large data repositories. We employ a fuzzy relational database as the medium carrying the original information, where the lack of precise information about an entity can be reflected via multiple attribute values, and the classical equivalence relation is replaced with relation of...

Knowledge Discovery in Databases