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Clustering algorithms are part of algorithms of unsupervised-learning that are commonly used in different fields. Two major impacts on the data clustering algorithm have been the rapid developments and creation of electronic data. This includes the view of how the big data is stored as well as how it is processed. A cluster has a high level of resemblance to the same cluster and a low level of resemblance to other clusters. Clustering algorithms are commonly used in all fields, such as retail, banking, development, etc.. Even though different methodologies are proposed with different scenarios, but none of the existing methodologies are proven to be a better approaches. Here, in our work we have adopted an improved Map- Reduce programming model to combine the Canopy clustering and K-means clustering algorithms to process the Microarray data with the available commodity hardware with an aim to achieve better performance. The results obtained from different scenarios shown that the proposed method was capable of improving computational speed significantly by increasing the nodes as required. In this research, this paper explored the efficiency by evaluating and implementing the proposed Improved k-means with Improved Map Reduce algorithm which runs on Hadoop Frame work using Microarray dataset along with different datasets. [ABSTRACT FROM AUTHOR]

Nowadays, organizations collect volumes of data for future analysis. Motivated by this amount of data and requirements of Web 2.0, a plethora of non-relational databases (NoSQL) emerged in recent years. However, several security features in relational databases (e.g., access control) have been left in non-relational management systems to be developed by the application, which can raise security breaches. This paper proposes two distinctive security model, based on the use of metadata, to provide access control for NoSQL graph-oriented database management system and organization based system. The goal is to support the development of applications that use graph-oriented database in preserving the integrity of stored data and protect them from non-authorized access. A case study has been performed as proof of concept, where the model has been instantiated and implemented for Neo4j database. A network management data security technique for executives based on NoSQL is suggested for better assurance of Organization based security systems. These are the two distinctive security models presented in this study. [ABSTRACT FROM AUTHOR]

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]

Image fusion may be defined as an approach of merging two or more input images in to a single fused image which is comparatively more informative than all the input images. Image fusion aims to reduce the redundancy and uncertainty in the output and aims to maximize application related significant information. Fused images are used in diverse applications such as remote sensing, machine vision, biometrics, medical imaging and military domain. In this paper, we carry out iterative image fusion through fuzzy logic and neuro fuzzy logic techniques by fusing satellite images obtained from two or more image sensors to improve visualization capability. We also compare the proposed iterative image fusion approach based on fuzzy logic and neuro fuzzy logic with two more approaches like Principal component analysis (PCA) based fusion and the wavelet transform based image fusion. Fused images obtained from iterative image fusion using fuzzy and neuro fuzzy and other comparative methods are assessed through typical quality evaluation metrics. The proposed iterative fusion based on fuzzy logic approach divulges better values in some metrics whereas iterative image fusion based on neuro fuzzy logic technique provides better values in other metrics. Due to the potentiality of the fuzzy logic and neuro fuzzy logic. The overall experimental results generated from suggested approach substantiate that the utilization of the iterative image fusion using fuzzy logic and neuro fuzzy logic techniques would efficiently improve the quality grade of the fused image with concurrent confinement of spectral and spatial information. [ABSTRACT FROM AUTHOR]