There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Data Mining - Concepts and Techniques.

Matrix Factorization Techniques for Recommender Systems

Most of the past document image watermarking schemes focus on providing same level of integrity and copyright protection for information present in the source document image. However, in a document image the information contents possess various levels of sensitivity. Each level of sensitivity needs different type of protection and this demands multiple watermarking techniques. In this paper, a novel intelligent multiple watermarking techniques are proposed. The sensitivity of the information content of a block is based on the homogeneity and relative energy contribution parameters. Appropriate watermarking scheme is applied based on sensitivity classification of the block. Experiments are conducted exhaustively on documents. Experimental results reveal the accurate identification of the sensitivity of information content in the block. The results reveal that multiple watermarking schemes has reduced the amount of data to be embedded and consequently improved perceptual quality of the watermarked image.

Computer Vision, Graphics, and Image Processing

It is crucial to determine the optimal number of clusters for the clustering quality in cluster analysis. From the standpoint of sample geometry, two concepts, i.e., the sample clustering dispersion degree and the sample clustering synthesis degree, are defined, and a new clustering validity index is designed. Moreover, a method for determining the optimal number of clusters based on an agglomerative hierarchical clustering (AHC) algorithm is proposed. The new index and the method can evaluate the clustering results produced by the AHC and determine the optimal number of clusters for multiple types of datasets, such as linear, manifold, annular, and convex structures. Theoretical research and experimental results indicate the validity and good performance of the proposed index and the method.

Method for Determining the Optimal Number of Clusters Based on Agglomerative Hierarchical Clustering

http://academic.oup.com/comjnl/advance-article-pdf/doi/10.1093/comjnl/bxab164/40655286/bxab164.pdf

Rare Correlated Coherent Association Rule Mining With CLS-MMS

Segmentation is no doubt a useful approach to solve an ambiguous situation. The domain of image segmentation deals with real life complexities. In this article we have proposed a new segmentation technique and named it as Vague Clustering Algorithm (VCA). Apart from the knowledge of fuzzy set this method is enriched with the concept of vague set. Fuzzy set theory treats the memberships in an exclusive way, so that the membership is a singleton, representing the fixed degree of belongings of an object to a cluster. The vague set represents the memberships in a different way, it defines the likelihood of belonging of an object to a cluster. Therefore, when fuzzy membership is represented by a single value between 0 and 1, the membership in a vague set is represented by a continuous sub interval of [0, 1]. In this article we have constructed a vague environment from the fuzzy environment which is followed by a type reduction. As vague set resembles an interval valued set, therefore type reduction essentially performs the defuzzification. We have compared the performance of our proposed VCA with the general Fuzzy C means (FCM) and Shadowed C means (SCM). To evaluate the performance, we choose different types of gray scale images and the experimental evidences prove the robustness of the proposed approach.

Gray Scale Image Segmentation with Vague Set

In this article, we have suggested an Artificial Bee Colony (ABC) inspired density based clustering. It can effectively deal with the issue of parameter selection as well as representation of varying densities of general density based clustering approaches. We plot the average density of the neighbours of each object in the data set. This unknown density distribution is then approximated using kernel density estimation. This helps us to identify the threshold, so that we can divide the density distribution of the data set into core and noise and we can also identify the cluster transitions. Including the parameter selection, the overall process is optimized using ABC. The superiority of the proposed approach has been delineated considering various data sets and by using different measurement techniques.

Artificial Bee Colony Inspired Density Based Clustering with efficient Parameter Selection

This article suggests a clustering approach that is inspired by the random searching of the Artificial Bee Colony optimization. It considers the density distribution of the objects which gives an insight into identifying the cluster structures. The unknown density distribution has been approximated by using Kernel Density Estimation, it eventually helps in identifying the thresholds that discriminate the core objects from the noise and intercluster density transition. We have introduced a new index measure that serves the purpose of cluster evaluation. The superiority of the proposed approach has been drawn from the experimental analysis over different data sets.

Kalyani Mali

Papers

Rare Correlated Coherent Association Rule Mining With CLS-MMS

Gray Scale Image Segmentation with Vague Set

Artificial Bee Colony Inspired Density Based Clustering with efficient Parameter Selection

An Artificial Bee Colony Inspired Density-Based Approach for Clustering with New Index Measure

Application of Fuzzy Sets and Shadowed Sets in Predicting Time Series Data