Data Mining Projects

This category consists of Data Mining Projects list, latest IEEE Papers on Data Mining Projects




    1. A Stock Market Prediction Model using Neural Network
    2. Abstract:
      The use of Neural network s has found a variegated field of applications in the present world. This has led to the development of various models for financial markets and investment. This paper represents the idea how to predict share market price using Artificial Neural Network with a given input parameters of share market. The share market is dynamic in nature means to predict share price is very complex process by general prediction or computation method. Its main reason is that there is no linear relationship between market parameters and target closing price. Since there is no linear relationship between input patterns and corresponding output patterns, so use of neural network is a choice of interest for share market prediction. Because this network in training phase learns about situations affecting share market price in a given environment. And this learnt knowledge stored in given network is used for predicting future market price. Artificial Neural Network can remember data of any number of years and it can predict the feature based on the past data. This paper makes use feed forward architecture for prediction. The network was trained using one year data. It shows a good performance for market prediction.

    3. CRPM - Data Mining-Based Institutional Performance System To Measure Candidate Potential
    4. Abstract:
      The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students' performance and so on. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. System is designed to justify the capabilities of data mining techniques in context of higher education by offering a data mining model for higher education system in the university. In this system, the classification and clustering is used to evaluate student's performance. For data classification, the decision tree method i.e ID3 algorithm is used and for clustering K-means algorithm is used. By this task we extract knowledge that describes student's performance in end semester examination. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling.

    5. Enabling Fine-grained Multi-keyword Similarity Search over Encrypted Cloud Data
    6. Abstract:
      With the advancement of information technologies particularly cloud storage used outsourcing data. Now a day's users store large amount of data on the cloud but it's an untrusted and we store secure data on the cloud. To overcome this drawback we propose the concept of searchable encryption provides a promising direction in solving the privacy problem when outsourcing data to the cloud. Such schemes allow users to store their data in encryption from at an untrusted server, and then delegate the server to search on their behalf by issuing a private key and encrypted search index.

    7. Friendbook A Semantic-Based Friend Recommendation System for Social Networks
    8. Abstract:
      Content based recommender systems have their roots in information retrieval and information filtering research. The content in these systems is usually described with keywords and the in formativeness of a keyword to a document is often measured by TF-IDF weight. Facebook relies on a social link analysis among those who already share common friends and recommends symmetrical users as potential friends. Unfortunately, this approach may not be the most appropriate based on recent sociology findings. We present Friend-book, a novel semantic-based friend recommendation system for social networks, which recommends friends to users based on their life styles instead of social graphs.

    9. Relational Collaborative Topic Regression for Recommender Systems
    10. Abstract:
      The objectives here are to change passive data into interactive data to enhance customer relationship management (CRM) while improving profitability. Customer attraction, retention and prediction are important marketing concepts in industrial & central components of data mining. Relational Collaborative Topic Regression for Recommender Systems is basically android application that impersonates all the operations of the restaurant for a customer-centric and profitable experience.

    11. Twitter Sentiment Analysis Using Hadoop
    12. Abstract:
      In this project, we investigate the utility of linguistic features for detecting the sentiment of Twitter messages using apache storm framework. We evaluate the usefulness of existing lexical resources as well as features that capture information about the informal and creative language used in microblogging. We take a supervised approach to the problem, but leverage existing hashtags in the Twitter data for building training data. In the past few years, there has been a huge growth in the use of microblogging platforms such as Twitter. Influenced by that growth, companies and media organizations are increasingly seeking ways to mine Twitter for information about what people think and feel about their products and services.

    13. Using Supervised Learning To Classify Clothing Brand Styles
    14. Abstract:
      A fashion style classification system can improve the customer search functionality and provide a more personalized experience for the user. Supervised learning techniques with fashion based applications face the problem of developing quantitative measures for describing fashion products which are subjective in nature. To address this issue, Quantitative measures are attributed to each brand in the training set by applying natural language processing, text mining, and eBay query results. This data set was used to train a support vector machine which classified the approximately 8000 remaining brands into style categories.

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