The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for searching the required data, particularly text documents. This is further facilitated by using Artificial...

The well-known B-tree data structure provides a mechanism for dynamically maintaining balanced binary trees in external memory. We present an external-memory dynamic data structure for maintaining arbitrary binary trees. Our data structure, which we call the topology B-tree, is an external-memory analogue to the internal-memory topol-ogy tree data structure of Frederickson. It allows for dynami...

The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for searching the required data, particularly text documents. This is further facilitated by using Artificial...

abstract saturated hydraulic conductivity (ks) is needed for many studies related to water and solute transport, but often cannot be measured because of practical and/or cost-related reasons. nonparametric approaches are being used in various fields to estimate continuous variables. one type of the nonparametric lazy learning algorithms, a k-nearest neighbor (k-nn) algorithm, was introduced and...

kernel density estimators are the basic tools for density estimation in non-parametric statistics.  the k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in  which  the  bandwidth  is varied depending on the location of the sample points. in this paper‎, we  initially introduce the k-nearest neighbor kernel density estimator in the random left-truncatio...

Finding Nearest Neighbors efficiently is crucial to the design of any nearest neighbor classifier. This paper shows how Layered Range Trees could be used for efficient nearest neighbor classification. The presented algorithm is simple and finds the nearest neighbor in a logarithmic order. It performs d log n + k distance measures to find the nearest neighbor, where k is a constant that is much ...

Memory-Based Reasoning and K-Nearest Neighbor Searching are frequently adopted data mining techniques. But, they suffer from scalability. Indexing is a promising solution. However, it is difficult to index categorical attributes, since there does not exist linear ordering property among categories in a nominal attribute. In this paper, we proposed heuristic algorithms to map categories to numbe...