V. Pattabiraman

Bio: V. Pattabiraman is an academic researcher from VIT University. The author has contributed to research in topics: Cluster analysis & Hyperspectral imaging. The author has an hindex of 5, co-authored 27 publications receiving 66 citations. Previous affiliations of V. Pattabiraman include PSG College of Arts and Science & TVS Motor Company.

A Novel Spatial Clustering with Obstacles and Facilitators Constraint Based on Edge Detection and K-Medoids

Abstract: Clustering spatial data is a well-known problem that has been extensively studied. Grouping similar data in large 2-dimensional spaces to find hidden patterns or meaningful sub-groups has many applications such as satellite imagery, geographic information systems, medical image analysis, marketing, computer visions, etc. Spatial clustering has been an active research area in Spatial Data Mining (SDM). Many methods on spatial clustering have been proposed in the literature, but few of them have taken into account constraints that may be present in the data clustering. In this paper, we discuss the problem of spatial clustering with obstacles constraints and propose a novel spatial clustering using edge detection method and K-Mediods, which objective is to cluster the spatial data (images) with the constraints and also comparing the result with the various constraints based clustering algorithms in terms of number of clusters and its execution time. The Edge detection based K-Mediods algorithms can not only given attention to higher speed and stronger global optimum search, but also get down to the obstacles and facilitator constraints and practicalities of spatial clustering. Taking into account these constraints during the clustering process is costly and the modeling of the constraints is paramount for good performance. The results on real datasets shown that the Edge detection based spatial clustering with the constraints are performs better than the existing constraint based clustering.

Data Mining Approach for Intelligent Customer Behavior Analysis for a Retail Store

Abstract: The Occurrence of the recent economic and social changes transformed the retail sector in particular the relationship between the customers and the retail stores changed significantly. In the past the retail industry focused on marketing the products without having detailed knowledge about the customers who availed products. With the proliferation of competitors the retail stores had to target on retaining their customers. To be successful in today’s competitive environment retail stores must creatively and innovatively meet their customer needs and expectations. Generic mass marketing messages are irrelevant. This paper put forwards, a new approach of customer classification based on the RFM(Mode) model and also deals with customer data to analyze and predict the customer behavior using clustering and association rule mining techniques.

Cat Swarm Optimization-Based Computer-Aided Diagnosis Model for Lung Cancer Classification in Computed Tomography Images

Abstract: Lung cancer is the most significant cancer that heavily contributes to cancer-related mortality rate, due to its violent nature and late diagnosis at advanced stages. Early identification of lung cancer is essential for improving the survival rate. Various imaging modalities, including X-rays and computed tomography (CT) scans, are employed to diagnose lung cancer. Computer-aided diagnosis (CAD) models are necessary for minimizing the burden upon radiologists and enhancing detection efficiency. Currently, computer vision (CV) and deep learning (DL) models are employed to detect and classify the lung cancer in a precise manner. In this background, the current study presents a cat swarm optimization-based computer-aided diagnosis model for lung cancer classification (CSO-CADLCC) model. The proposed CHO-CADLCC technique initially pre-process the data using the Gabor filtering-based noise removal technique. Furthermore, feature extraction of the pre-processed images is performed with the help of NASNetLarge model. This model is followed by the CSO algorithm with weighted extreme learning machine (WELM) model, which is exploited for lung nodule classification. Finally, the CSO algorithm is utilized for optimal parameter tuning of the WELM model, resulting in an improved classification performance. The experimental validation of the proposed CSO-CADLCC technique was conducted against a benchmark dataset, and the results were assessed under several aspects. The experimental outcomes established the promising performance of the CSO-CADLCC approach over recent approaches under different measures.

Image Processing

Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

Hyperspectral Imaging Techniques For Spectral Detection And Classification

Abstract: Hyperspectral Imaging: Techniques for Spectral Detection and Classification is an outgrowth of the research conducted over the years in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. It explores applications of statistical signal processing to hyperspectral imaging and further develops non-literal (spectral) techniques for subpixel detection and mixed pixel classification. This text is the first of its kind on the topic and can be considered a recipe book offering various techniques for hyperspectral data exploitation. In particular, some known techniques, such as OSP (Orthogonal Subspace Projection) and CEM (Constrained Energy Minimization) that were previously developed in the RSSIPL, are discussed in great detail. This book is self-contained and can serve as a valuable and useful reference for researchers in academia and practitioners in government and industry.

Customer segmentation by using rfm model and clustering methods: a case study in retail industry

Abstract: In today’s business environment companies should need better understanding on customers’ data. Detecting similarities and differences among customers, predicting their behaviors, proposing better options and opportunities to customers, etc. became very important for customer-company engagement. Segmenting customers according to their data became vital in this context. RFM (recency, frequency and monetary) values have been used for many years to identify which customers valuable for the company, which customers need promotional activities, etc. Data-mining tools and techniques commonly have been used by organizations and individuals to analysis their stored data. Clustering, which one of the tasks of data mining has been used to group people, objects, etc. In this paper we propose two different clustering models to segment 700032 customers by considering their RFM values. We suggest that the current customer segmentation which built by just considering customers’ expense is not sufficient. Hence, one of the models that recommended in this research is expected to provide better customer understanding, well-designed strategies, and more efficient decisions.