Bettahally N. Keshavamurthy

Privacy preserving association rule mining over distributed databases using genetic algorithm

Privacy preservation in distributed database is an active area of research. With the advancement of technology, massive amounts of data are continuously being collected and stored in distributed database applications. Indeed, temporal associations and correlations among items in large transactional datasets of distributed database can help in many business decision-making processes. One among them is mining frequent itemset and computing their association rules, which is a nontrivial issue. In a typical situation, multiple parties may wish to collaborate for extracting interesting global information such as frequent association, without revealing their respective data to each other. This may be particularly useful in applications such as retail market basket analysis, medical research, academic, etc. In the proposed work, we aim to find frequent items and to develop a global association rules model based on the genetic algorithm (GA). The GA is used due to its inherent features like robustness with respect to local maxima/minima and domain-independent nature for large space search technique to find exact or approximate solutions for optimization and search problems. For privacy preservation of the data, the concept of trusted third party with two offsets has been used. The data are first anonymized at local party end, and then, the aggregation and global association is done by the trusted third party. The proposed algorithms address various types of partitions such as horizontal, vertical, and arbitrary.

Privacy Preserving Naïve Bayes Classification Using Trusted Third Party Computation over Distributed Progressive Databases

Privacy-preservation in distributed progressive databases is an active area of research in recent years. In a typical scenario, multiple parties may wish to collaborate to extract interesting global information such as class labels without revealing their respective data to each other. This may be particularly useful in applications such as customer retention, medical research etc. In the proposed work, we aim to develop a global classification model based on the Naive Bayes classification scheme. The Naive Bayes classification has been used because of its simplicity, high efficiency. For privacy-preservation of the data, the concept of trusted third party with two offsets has been used. The data is first anonymized at local party end and then the aggregation and global classification is done at the trusted third party. The proposed algorithms address various types of fragmentation schemes such as horizontal, vertical and arbitrary distribution required format. The car-evaluation dataset is used to test the effectiveness of proposed algorithms.

Hiding co-occurring prioritized sensitive patterns over distributed progressive sequential data streams

Recently, privacy preservation in data mining is an important area of research. It can be done in several ways. Hiding of sensitive patterns is one such important method. In a typical scenario, multiple parties may wish to collaborate to extract interesting global patterns from their integrated data items without revealing their respective local data to each other. Typical applications include finance, medical research, retail sales etc. In certain cases, there may be some patterns whose co-occurrence may lead to revelation of sensitive information. In the present work, hiding of co-occurring sensitive patterns dynamically from distributed progressive databases has been proposed. In addition in the proposed work dynamic priorities have also been coupled, along with the items. This helps to decide which patterns to hide from the set of sensitive patterns. The various partitioning scenarios for distributed databases that have been used include horizontal, vertical and arbitrary. In all such cases, the data is distributive progressive in nature i.e., old data items may become obsolete whereas new data items may be treated as more significant.

EFFICIENT SUPPORT COUPLED FREQUENT PATTERN MINING OVER PROGRESSIVE DATABASES

There have been many recent studies on sequential pattern mining. The sequential pattern mining on progressive databases is relatively very new, in which we progressively discover the sequential patterns in period of interest. Period of interest is a sliding window continuously advancing as the time goes by. As the focus of sliding window changes, the new items are added to the dataset of interest and obsolete items are removed from it and become up to date. In general, the existing proposals do not fully explore the real world scenario, such as items associated with support in data stream applications such as market basket analysis. Thus mining important knowledge from supported frequent items becomes a non trivial research issue. Our proposed novel approach efficiently mines frequent sequential pattern coupled with support using progressive mining tree.

Feature Extraction Model for Social Images

Extraction of appropriate features is a difficult task because it mainly depends on a specific application domain. In this paper, we presented a 5-layered feature extraction model for social images. This model extracts color, texture, geometric, and regional features from given image and also checks presence or absence of people in an image by face detection. Then, normalization of the feature vector is done with the help of priority element. Proposed model is able to deal with the heterogeneous nature of social images. It is useful to get good results in the field of social data analytics.

Privacy-Preserving Naïve Bayes Classification Using Trusted Third Party Computation over Vertically Partitioned Distributed Progressive Sequential Data Streams

Privacy preservation in distributed progressive data stream is an active area of research in the present time. In a typical scenario, multiple parties may be wishing to collaborate to extract interesting global information such as class labels without breaching privacy. This may be particularly useful in applications such as customer retention, medical research etc. In the present work, we aim to develop a global classification model based on the Naive Bayes classification scheme. The Naive Bayes classification has been used because of its applicability in case of customer retention application such as car evaluation dataset. For privacy preservation of the data, the concept of trusted third party has been used. We have proposed algorithms and tested car evaluation dataset for vertical partitioned progressive sequential data streams.

Privacy-Preserving Naïve Bayes Classification Using Trusted Third Party and Offset Computation over Distributed Databases

Privacy-preservation in distributed databases is an important area of research in recent years. In a typical scenario, multiple parties may be wish to collaborate to extract interesting global information such as class labels without revealing their respective data to each other. This may be particularly useful in applications such as car selling units, medical research etc. In the proposed work, we aim to develop a global classification model based on the Naive Bayes classification scheme. The Naive Bayes classification has been used because of its applicability in case of car-evaluation dataset. For privacy-preservation of the data, the concept of trusted third party with different offset has been used. The data is first anonymized at local party end and then the aggregation and global classification is done at the trusted third party. We have proposed algorithms and tested dataset for different distributed database scenarios such as horizontal, vertical and arbitrary partitions.

An Efficient Approach for Classifying Social Network Events Using Convolution Neural Networks

The rise of social media and social platforms has led to enormous information dissemination. Images shared by social users at any moment convey what they see or where they have been to. Social images express far more information than texts which may involve individuals’ characteristics like personality traits. Existing methods perform event classification based on fixed temporal and spatial image resolutions. In this paper, we thoroughly analyse social network images for event classification using Convolution Neural Networks (CNNs). CNN captures both important patterns and their contents, to extract the semantic information from the images. We collect shared images from Flicker specifying various sports events that users attend. Images are divided into three event classes, i.e. bikes, water and ground. After extensive experimentation using CNN, for training and classifying images, we obtain an accuracy of 98.7%.

Fashion image classification using matching points with linear convolution

Social image data related to fashion is flowing through the social networks in huge amount. Analysis of this data is a challenging task due to its characteristics like voluminous, unstructured, etc. Classification provides an easy and efficient way to deal with such data. In this paper, we proposed a new approach for classification of fashion images by incorporating the concepts of linear convolution and matching points using local features. Linear convolution is used to get the representative images with important features. Then, matching points between given image and class representative images are obtained. Maximum matching points are considered while assigning a class label to the given image. Proposed approach is useful further for various applications related to fashion such as fashion recommendation, fashion trend analysis, etc.

A survey on image data analysis through clustering techniques for real world applications

Abstract A huge amount of image data is being collected in real world sectors. Image data analytics provides information about important facts and issues of a particular domain. But, it is challenging to handle voluminous, unstructured and unlabeled image collection. Clustering provides groups of homogeneous unlabeled data. Therefore, it is used quite often to access the interesting data easily and quickly. Image clustering is a process of partitioning image data into clusters on the basis of similarities. Whereas, features extracted from images are used for the computation of similarities among them. In this paper, significant feature extraction approaches and clustering methods applied on the image data from nine important applicative areas are reviewed. Medical, 3D imaging, oceanography, industrial automation, remote sensing, mobile phones, security and traffic control are considered applicative areas. Characteristics of images, suitable clustering approaches for each domain, challenges and future research directions for image clustering are discussed.