A Review on Hybrid Approach Based on K-Mean and Ward’s Algorithm

Abstract

Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. In this paper, a study based on K-means and Ward's Algorithm with Honey Bee optimization is done for spatial data mining and finally an algorithm is created for data clustering also. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining. So, by this algorithm clustering can be done in a most appropriate way and can be used for further study. Keywords: Data clustering, Data Mining, Algorithm, Honey Bee Optimization I.INTRODUCTION Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from spatial databases. It is basically holds 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 [1] [2]. Moreover Geographical information systems are becoming rich deposits of spatial data in many application areas (i.e., geology, meteorology, traffic planning, emergency aids). The GISs provide the user with the possibility of querying a territory for extracting areas that exhibit certain properties, i.e., given combinations of values of the attributes. This explosively growing spatial data creates the necessity of knowledge/information discovery from spatial data, which leads to a promising emerging field, called spatial data mining or knowledge discovery in spatial databases [3]. Regionalization has been an important and challenging problem for a large spectrum of research and application domains, for example, climatic zoning [5], eco region analysis [6], hazards and disasters management [7], map generalization [8], location optimization [9], census reengineering [10], and health-related analysis [11]. Regionalization is essentially a special form of classification where spatial units are grouped together, based on a set of defined criteria and a set of contiguity or adjacency constraints [12]. Spatial clustering is an important component of spatial data mining. It aims group similar spatial objects into group or clusters so that objects within a cluster have high similarity in comparison to one another but are dissimilar to objects in other clusters [13]. Spatial clustering can be applicable for solving many problems. An important application area for the spatial clustering algorithm is social and economic geography. In the scope a classical methodical problem of social geography, "re...