Data Mining - Concepts and Techniques.

International Journal of Computer and Applications

A morphable model for the synthesis of 3D faces

Power Aware Routing in Mobile Ad Hoc Networks

A privacy preserving association rule mining algorithm was introducedThis algorithm preserved privacy of individual values by computing scalar productMeanwhile the algorithm of computing scalar product was given and the security was analyzed

Privacy preserving association rule mining in vertically partitioned data

Consumer insight mining: Aspect based Twitter opinion mining of mobile phone reviews

Knowledge Discovery and Data Mining(KDD) process includes preprocessing, transformation, data mining and knowledge extraction. The two important tasks of data mining are clustering and classification. In this paper, we propose a generic feature extraction for classification using Fuzzy C-Means(FCM) clustering. The raw data is preprocessed, normalized and then data points are clustered using fuzzy c-means technique. Feature vectors for all the classes are generated by extracting the most relevant features from the corresponding clusters and used for further classification. Artificial Neural Network and Support Vector Machines are used to perform the classification task. Experiments are conducted on four datasets and the accuracy obtained by performing specific feature extraction for a particular data set is compared with generic feature extraction scheme. The algorithm performs relatively well with respect to classification results when compared with the specific feature extraction technique.

/pdf/feature-extraction-using-fuzzy-c-means-clustering-for-data-2vciqpcnsm.pdf

Feature Extraction using Fuzzy C - Means Clustering for Data Mining Systems

The modern steganography presents a challenging task of embedding data that should be imperceptible to the human visual system (HVS) and also escape the detection of powerful machine vision of computers. In this paper we present a high capacity, lossless, secure wavelet steganographic algorithm in which payload bitstream is encrypted and embedded into the wavelet coefficients of the cover image to derive a stego-image. The payload is embedded in the approximation band of the wavelet domain that increases its robustness. It is observed through simulations that mean square error (MSE), mean absolute error (MAE), bit error rate (BER) and histogram analysis that the pay load is retrieved without any errors and its performance is better than the earlier insignificant coefficient replacement (ICR) technique.

High Capacity Lossless Secure Image Steganography using Wavelets

Video Segmentation is one of the most challenging areas in Multimedia Mining. It deals with identifying an object of interest. It has wide ap- plication in the fields like Traffic surveillance, Secu- rity, Criminology etc. This paper initially proposes a technique for identifying a moving object in a video clip of stationary background for real time content based multimedia communication systems and dis- cusses one application like traffic surveillance. We present a framework for detecting some important but unknown knowledge like vehicle identification and traffic flow count. The objective is to monitor ac- tivities at traffic intersections for detecting conges- tions, and then predict the traffic flow which assists in regulating traffic. The algorithm for vision-based detection and counting of vehicles in monocular im- age sequences for traffic scenes are recorded by a sta- tionary camera. Dynamic objects are identified using both background elimination and background regis- tration techniques. Post processing techniques are applied to reduce the noise. The background elimina- tion method uses concept of least squares to compare the accuracies of the current algorithm with the al- ready existing algorithms. The background registra- tion method uses background subtraction which im- proves the adaptive background mixture model and makes the system learn faster and more accurately, as well as adapt effectively to changing environments. Keywords—Background elimination, Frame differ- ence, Object identification, Background registration, Camera calibration, Vehicle tracking.

/pdf/dynamic-object-detection-tracking-and-counting-in-video-34x97yqvxx.pdf

Dynamic Object Detection, Tracking and Counting in Video Streams for Multimedia Mining

Medical data mining has great potential for exploring the hidden patterns in the data sets of the medical domain. These patterns can be utilized for the classification of various diseases. Data mining technology provides a user-oriented approach to novel and hidden patterns in the data. The present study consisted of records of 746 patients collected from ADRC, ISTAART, USA. Around eight hundred and ninety patients were recruited to ADRC and diagnosed for Alzheimer's disease (65%), vascular dementia (38%) and Parkinson's disease (40%), according to the established criteria. In our study we concentrated particularly on the major risk factors which are responsible for Alzheimer's disease, vascular dementia and Parkinson's disease. This paper proposes a new model for the classification of Alzheimer's disease, vascular disease and Parkinson's disease by considering the most influencing risk factors. The main focus was on the selection of most influencing risk factors for both AD and PD using various attribute evaluation scheme with ranker search method. Different models for the classification of AD, VD and PD using various classification techniques such as Neural Networks (NN) and Machine Learning (ML) methods were also developed. It is observed that increase in the vascular risk factors increases the risk of Alzheimer's disease. It was found that some specific genetic factors, diabetes, age and smoking were the strongest risk factors for Alzheimer's disease. Similarly, for the classification of Parkinson's disease, the risk factors such as stroke, diabetes, genes and age were the vital factors.

/pdf/classification-of-neurodegenerative-disorders-based-on-major-wlnq3u9anr.pdf

L M Patnaik

Papers

Consumer insight mining: Aspect based Twitter opinion mining of mobile phone reviews

Feature Extraction using Fuzzy C - Means Clustering for Data Mining Systems

High Capacity Lossless Secure Image Steganography using Wavelets

Dynamic Object Detection, Tracking and Counting in Video Streams for Multimedia Mining

Classification of Neurodegenerative Disorders Based on Major Risk Factors Employing Machine Learning Techniques