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Remote sensing techniques have been extensively utilized for recognition of land use and land cover structures. Land evidence can be definitely composed by classification of satellite images in the perspective of their practice. In this paper study area has been classified into five classes i.e. vegetation, agriculture, water body, open area and urban land by classification of fused images obtained from various fusion techniques. The spatial and spectral determinations of various satellite images make availableimprovedevidence with the encouragementof imageprocessing and image fusion of both multispectral and spatial images. The input images fused together are multispectral image and panchromatic images obtained from IRS-1D satellite utilizing LISS III. Matlab 10.0 software has been used for image processing, fusion and classification of the images. The Principal Component Analysis (PCA), wavelet transform, fuzzy and neuro fuzzy techniques arehave been used for image fusion. The resultant images have been classified using the supervised and unsupervised classification techniques;decision tree classifier and K-Meansalgorithms and evaluationconcerning them in standings of their accuracy. [ABSTRACT FROM AUTHOR]

Frequent itemsets plays a crucial role in many data mining tasks that aimed to find interesting patterns. Frequent Pattern mining is used for find frequent itemsets among items in a given data set and the exact frequency of each itemset. An objective of frequent pattern mining is to develop a systematic method for the given data set and find frequent items. Obtained frequent items can be used for enhancing the business return of interest. The Synoptic/Concise representation of frequent Itemsets sacrifice/presents readability and direct interpretability by a data analyst of the concise patterns extracted/picked up. This paper presents, FRI Miner, a tool for Frequent Regular Itemsets Mining, designed to evaluate RegularMine and FPGrowth, and experimented on real time datasets. [ABSTRACT FROM AUTHOR]

Spatial databases store information related to objects positional locations e.g., various kinds of multidimensional objects represented by points, line segments, polygons and other kinds of geometric entities. These databases should support for efficient storage, indexing and querying of spatial data. Special purpose indexing structures are important for accessing the spatial data, and for processing spatial queries. To handle the spatial data efficiently, a database system needs an indexing mechanism that will help it to retrieve data items quickly according to their spatial locations instead of using GIS (Geospatial Information System). Hence, in this paper, authors evaluated R, R+ and R* dynamic indexing algorithms, on a variety of real time and synthetic datasets, to access the spatial data. From the experimental study, one can observe that the R* indexing technique performs well (than R and R+) for spatial databases. [ABSTRACT FROM AUTHOR]