Urban hotspot area detection is an important issue that needs to be explored for urban planning and traffic management. It of great significance mine hotspots from taxi trajectory data, which reflect residents’ travel characteristics the operational status traffic. The existing clustering methods mainly concentrate on number objects contained in within a specified size, neglecting impact local ...

Nearest neighbor classification expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensions We propose a locally adaptive form of nearest neighbor classification to try to finesse this curse of dimensionality. We use a local linear discriminant analysis to estimate an effective metric for computing neighborhoods. We determine the local decision b...

In this study an attempt was made to understand the structural requirements for Topoisomerase I (Topo I) inhibition using a novel Group based QSAR (GQSAR) or fragment based QSAR technique. Here we combined the GQSAR technology with conventional 2D and 3D QSAR to derive GQSAR models for various reported naphthoquinone derivatives. Various regression models such as Multiple Regression (MRA), Part...

The main purpose of this paper is to introduce a concept of L-fuzzifying topological vector spaces (here L is a completely distributive lattice) and study some of their basic properties. Also, a characterization of such spaces in terms of the corresponding L-fuzzifying neighborhood structure of the zero element is given. Finally, the conclusion that the category of L-fuzzifying topological vect...

Within the Geospatial Semantic Web, selecting a different ontology for a spatial data set will enable that data’s analysis in a different context. Analyses of multiple data sets, each based on a different ontology, require appropriate bridges across the ontologies. This paper focuses on establishing such a bridge across two topological-relation ontologies of different granularity—the standard e...

By exploiting the description of topological spaces by either neighborhood systems or filter convergence, we obtain a neighborhood-like presentation of categories of lax algebras. The simplicity of this presentation pinpoints the importance of the Kleisli extension, which is introduced as a particular lax extension of the associated monad functor.

We study clustering algorithms based on neighborhood graphs on a random sample of data points. The question we ask is how such a graph should be constructed in order to obtain optimal clustering results. Which type of neighborhood graph should one choose, mutual k-nearest neighbor or symmetric k-nearest neighbor? What is the optimal parameter k? In our setting, clusters are defined as connected...

Topological relationships between spatial objects are considered to be important for spatial databases. They lead to topological predicates, which can be embedded into spatial queries as join or selection conditions. Before rushing into the implementation of topological predicates, topological relationships between spatial objects must be first understood and clarified. This requires a detailed...

The theory of neighborhood systems is abstracted from the geometric notion of "near" or "negligible distances." It is a "new" theory of the classical concept of neighborhood systems within the context of advanced computing. By definition neighborhood systems include both rough sets and topological spaces as special cases. The deeper and more interesting part is in its interactions with fuzzy se...

K nearest neighbor classifier (K-NN) is widely discussed and applied in pattern recognition and machine learning, however, as a similar lazy classifier using local information for recognizing a new test, neighborhood classifier, few literatures are reported on. In this paper, we introduce neighborhood rough set model as a uniform framework to understand and implement neighborhood classifiers. T...