2018-2019 Data Mining Projects for Final Year

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2018-2019 Data mining Based Projects for CSE

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2018-2019 Data mining projects for cse students

1.A New Query Recommendation Method Supporting Exploratory Search Based on Search Goal Shift Graphs

2.Approximate Order-Sensitivek-NN Queries over Correlated High-Dimensional Data

3.BEATS: Blocks of Eigenvalues Algorithm for Time Series Segmentation

4.Classifier Ensemble by Exploring Supplementary Ordering Information

5.Efficient Detection of Overlapping Communities Using Asymmetric Triangle Cuts

6.High-Order Proximity Preserved Embedding for Dynamic Networks

7.Identifying Genetic Risk Factors for Alzheimer's Disease via Shared Tree-Guided Feature Learning Across Multiple Tasks

8.NHAD: Neuro-Fuzzy Based Horizontal Anomaly Detection in Online Social Networks

Data mining projects using Java

2018-2019 IEEE Projects for CSE Based on Data Mining

2018-Privacy Policy Inference of User-Uploaded Images on Content Sharing Sites
Abstract—With the increasing volume of images users share through social sites, maintaining privacy has become a major problem, as demonstrated by a recent wave of publicized incidents where users inadvertently shared personal information. In light of these incidents, the need of tools to help users control access to their shared content is apparent. Toward addressing this need, we propose an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. We examine the role of social context, image content, and metadata as possible indicators of users’ privacy preferences. We propose a two-level framework which according to the user’s available history on the site, determines the best available privacy policy for the user’s images being uploaded. Our solution relies on an image classification framework for image categories which may be associated with similar policies, and on a policy prediction algorithm to automatically generate a policy for each newly uploaded image, also according to users’ social features. Over time, the generated policies will follow the evolution of users’ privacy attitude. We provide the results of our extensive evaluation over 5,000 policies, which demonstrate the effectiveness of our system, with prediction accuracies over 90 percent.

2018-2019 IEEE Projects for cse in Data Mining

2018-Context-based Diversification for Keyword Queries over XML Data

Abstract—While keyword query empowers ordinary users to search vast amount of data, the ambiguity of keyword query makes it difficult to effectively answer keyword queries, especially for short and vague keyword queries. To address this challenging problem, in this paper we propose an approach that automatically diversifies XML keyword search based on its different contexts in the XML data. Given a short and vague keyword query and XML data to be searched, we firstly derive keyword search candidates of the query by a simple feature selection model. And then, we design an effective XML keyword search diversification model to measure the quality of each candidate. After that, two efficient algorithms are proposed to incrementally compute top-k qualified query candidates as the diversified search intentions. Two selection criteria are targeted: the k selected query candidates are most relevant to the given query while they have to cover maximal number of distinct results. At last, a comprehensive evaluation on real and synthetic datasets demonstrates the effectiveness of our proposed diversification model and the efficiency of our algorithms.


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