Mining Spatial Data&Enhancing Classification Using Bio - Inspired Approaches

Data-Mining (DM) has become one of the most valuable tools for extracting and manipulating data and for establishing patterns in order to produce useful information for decision-making. It is a generic term that is used to find hidden patterns of data (tabular, spatial, temporal, spatio-temporal etc. ) Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from spatial databases Extracting interesting and useful patterns from spatial datasets is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationship and spatial autocorrelation. Spatial data are the data related to objects that occupy space. A spatial database stores spatial objects represented by spatial data types and spatial relationship among such objects. Clustering is the process of partitioning a set of data objects into subsets such that the data elements in a cluster are similar to one another and different from the element of other cluster The set of cluster resulting from a cluster analysis can be referred to as a clustering. Spatial clustering is a process of grouping a set of spatial objects into clusters so that objects within a cluster have high similarity in comparison to one another, but are dissimilar to objects in other clusters. In this paper, enhancement of classification scheme is done using various Honey bee Optimization and Firefly Optimization. There are number of artificial intelligence techniques which helps in data mining to get the optimized result of the query. Hybrid of K-Mean& Wards Method, Honeybee Optimization and Firefly Optimization will be compared on the basis of performance parameters of classification (precision, recall, cohesion, variance, F-Measure, H-Measure) and therefore enhancement will be done.