Data Mining and Knowledge Engineering

S. Bharathi ¹, P. Deepa Shenoy ², K. R. Venugopal ³, L. M. Patnaik ⁴

Affiliations
1 Department of MCA, Dr Ambedkar Institute of Technology, Bangalore, IN
2 Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore, IN
3 University Visvesvaraya College of Engineering, Bangalore University, Bangalore, IN
4 Indian Institute of Science, Bangalore, IN

Abstract

The task of indentifying similar objects within the querying image remains challenging. It is due to viewpoint and lighting changes, deformation and partial occlusions that may exist across different examples. In this framework we focus on combination methods that ensemble multiple descriptors at multiple spatial resolution levels. Ensemble PHOG (Pyramid histogram orientation and gradient) and SIFT (Scale invariant feature transformation) descriptors are used for the feature extraction to achieve the good classification accuracy. Within a region local feature was captured by the distribution over edge orientation, and spatial layout by tiling the image into regions at multiple resolutions. The SIFT features are extracted for each PHOG block. These features are trained using SOM network. Later SVM and Neural network classifiers are used for classification. Results demonstrating the effectiveness of the proposed technique are provided using confusion matrix, transition matrix and other accuracy measures. Area of different land cover regions are calculated, which can be used for land use changes.

Keywords

PHOG, SIFT, Classification, Satellite Image.

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J. S. Saleema ¹, P. Deepa Shenoy ², K. R. Venugopal ³, L. M. Patnaik ⁴

Affiliations
1 Dept. of Comp. Sci., Christ University, Bangalore, IN
2 Department of CSE, University Visvesvaraya College of Engineering, Bangalore, IN
3 University Visvesvaraya College of Engineering, Bangalore, IN
4 Indian Institute of Science, Bangalore, IN

Abstract

Cancer prognosis prediction improves the quality of treatment and increases the survivability of the patients. Disease prognosis is identified at the treatment stage and at the recurrence stage. Conventional cancer prediction method deals only with the survival or mortality of the patients, but not with other labels such as severity of the disease through metastasis or multi-primary, stage, grade, etc. The SEER Public Use cancer database has more prominent variables that support better prediction approach. The objective of this paper is twofold. One is to build a prediction model to find the prominent variables by using the standard classifiers and the second is to improve the prediction accuracy through various sampling techniques. The proposed prediction model consist of three phases namely, basic level pre-processing, problem specific processing and modeling classifiers. Problem specific processing phase deals with feature extraction, sampling and response variable selection. The well known classification algorithms (Decision Tree, Naive Bayes and KNN) have been used to model the classifiers for prediction analysis. Apart from the available incident data from SEER (Breast, Colorectal and Respiratory Cancer data) a new mixed combination of the three in equal proportion have been generated for the experimentation. Feature selection through correlation and information gain reduced the attributes to 37 from the raw size of 118. Patient survival, age at diagnosis, stage and multiple primaries in the given order has been identified as the prominent response variable, where as grade performed very low in the experimentation. The performances of various sampling techniques have been studied with the data set size ranging from 500 to 30000 samples for the four prominent labels identified in the previous step. The result shows that the balanced stratified sampling technique always maintains consistency in the performance. Also classifier model with decision tree algorithm optimizes the performance compared to the other algorithms. All the results of the models are tabulated in this paper.

Keywords

Classifier, Pre-Processing, Prognosis Prediction, SEER.

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N. P. Nethravathi ¹, Vaibhav J. Desai ², P. Deepa Shenoy ², M. Indiramma ³, K. R. Venugopal ²

Affiliations
1 Visvesvaraya Technological University, Belagavi-590 018, IN
2 University Visvesvaraya College of Engineering, Bangalore University, Bangalore, IN
3 BMS College of Engineering, Bangalore-560019, IN

Abstract

With the onset of the digital revolution, organizations are increasingly maintaining a huge amount of information on their databases and use data mining tools to extract useful information for their business intelligence. The problem with the availability of the digital information is the scarce privacy leakage. In many business domains, leakage of personal information of the client either directly or through data mining tools can lead to loss of competitive edge of the company, loss of revenue and customer churn. Companies are pushing for encryption and other data transformation methods to keep the data private. But mining tools which invoke algorithms like clustering, classification etc. may not work properly on the transformed data. In this paper, we analyze the privacy preserving data mining solutions and privacy leakage in them through indirect means. The main objective of this paper is to identify the open areas of research on privacy-preserving data mining.

Keywords

Transformation Strategy, Privacy Preserving Data Mining, Cryptography, Wavelet Transformation, Correlation Analysis.

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