Web Application for Consultant Services

The emergence of internet has changed the system from the circulation of data that has shifted us from a world of paper documents to a world of online documents and databases systems.Consultancy services provide options for multiple different domains to be covered under one place. To be exact multiple services are provided under one company that acts as consultancy. Data mining plays an important role in many decision making application and research domains. Predictions of a things based on data available is one of the important features of data mining. Loan and insurance recommendation system is one of data mining and machine learning application where the system needs to recommend the banks that can provide loan to users and at the same time provide users with insurance providing companies that can provide proper scheme to users. We will use K-NN based approach for providing users with such recommendations. The K-NN algorithm performs analysis on that data. Based on the result of analysis, description of suitable financial services and insurance services will be displayed to the user.Finally it guides the user so that they can register themselves for those insurance policies which they find suitable

Outlier detection in large high-dimensional data and its application in stock market surveillance

University of Technology, Sydney. Faculty of Engineering and Information Technology.Outlier detection techniques play an important role in stock market surveillance that involves analysis of large volume of high-dimensional trading data. However, outlier detection in large high-dimensional data is very challenging and is not well addressed by existing techniques. Firstly, it is difficult to select useful and relevant features from high-dimensional data. Secondly, large high-dimensional data need more efficient algorithms. To attack the above issues brought by large high-dimensional data, this thesis presents two outlier detection models and one subspace clustering model. Firstly, an outlier mining model is proposed to detect the outliers from multiple complex stock market data. In order to improve the efficiency of outlier detection, a financial model is used to select the features to construct multiple datasets. This model is able to improve the precision of outlier mining on individual measurements. The experiments on real-world stock market data show that the proposed model is effective and outperforms traditional technologies. Secondly, in order to find relevant features automatically, an agent-based algorithm is proposed to discover subspace clusters in high dimensional data. Each data object is represented by an agent, and the agents move from one local environment to another to find optimal clusters in subspaces. Heuristic rules and objective functions are defined to guide the movements of agents, so that similar agents (data objects) go to one group. The experimental results show that our proposed agent-based subspace clustering algorithm performs better than existing subspace clustering methods on both F1 measure and Entropy. The running time of our algorithm is scalable with the size and dimensionality of data. Furthermore, an application of our technique to stock market surveillance demonstrates its effectiveness in real world applications. Finally, we propose a reference-based outlier detection model by agent-based subspace clustering. At first, agent-based subspace clustering is utilized to generate clusters in subspaces. After that, the centers of clusters, together with the corresponding subspaces, are used as references, and a reference-based model is employed to find outliers in relevant subspaces. The experimental results on real-world datasets prove that the proposed model is able to effectively and efficiently identify outliers in subspaces. In summary, this thesis research on outlier detection techniques on high-dimensional data and its application in stock market surveillance. The proposed models are novel and effective. They have shown their potentials in real business

Student Mining Using K-Means Clustering: A Basis for Improving Higher Education Marketing Strategies

This study aims to enhance marketing strategies in higher education institutions by applying data mining techniques, specifically K-means clustering. The research focuses on Mindanao State University - Lanao del Norte Agricultural College (MSU-LNAC), a tertiary institution in Northern Mindanao, Philippines, with the objective of increasing enrollment. The study utilizes the K-means algorithm to group attributes into different clusters. The clustering analysis provides valuable insights into the characteristics and preferences of the surveyed student population. Based on the findings, recommendations are presented to guide targeted marketing efforts, such as geographic targeting, collaborations with senior high schools, financial assistance programs, and the development of marketing campaigns that emphasize the institution's strengths and advantages. By implementing these recommendations, MSU-LNAC can enhance its recruitment and marketing strategies to attract and retain students effectively

Development and testing of a database of NIH research funding of AAPM members: A report from the AAPM Working Group for the Development of a Research Database (WGDRD).

PURPOSE: To produce and maintain a database of National Institutes of Health (NIH) funding of the American Association of Physicists in Medicine (AAPM) members, to perform a top-level analysis of these data, and to make these data (hereafter referred to as the AAPM research database) available for the use of the AAPM and its members. METHODS: NIH-funded research dating back to 1985 is available for public download through the NIH exporter website, and AAPM membership information dating back to 2002 was supplied by the AAPM. To link these two sources of data, a data mining algorithm was developed in Matlab. The false-positive rate was manually estimated based on a random sample of 100 records, and the false-negative rate was assessed by comparing against 99 member-supplied PI_ID numbers. The AAPM research database was queried to produce an analysis of trends and demographics in research funding dating from 2002 to 2015. RESULTS: A total of 566 PI_ID numbers were matched to AAPM members. False-positive and -negative rates were respectively 4% (95% CI: 1-10%, N = 100) and 10% (95% CI: 5-18%, N = 99). Based on analysis of the AAPM research database, in 2015 the NIH awarded $USD 110M to members of the AAPM. The four NIH institutes which historically awarded the most funding to AAPM members were the National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Heart Lung and Blood Institute, and National Institute of Neurological Disorders and Stroke. In 2015, over 85$116M, which is lower than the historic mean of $120M (in 2015 USD). CONCLUSIONS: A database of NIH-funded research awarded to AAPM members has been developed and tested using a data mining approach, and a top-level analysis of funding trends has been performed. Current funding of AAPM members is lower than the historic mean. The database will be maintained by members of the Working group for the development of a research database (WGDRD) on an annual basis, and is available to the AAPM, its committees, working groups, and members for download through the AAPM electronic content website. A wide range of questions regarding financial and demographic funding trends can be addressed by these data. This report has been approved for publication by the AAPM Science Council

Intelligent Financial Fraud Detection Practices: An Investigation

Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transactions in an attempt to discover fraudulent behaviour. This method is not only time consuming, expensive and inaccurate, but in the age of big data it is also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques. Classification of the practices based on key aspects such as detection algorithm used, fraud type investigated, and success rate have been covered. Issues and challenges associated with the current practices and potential future direction of research have also been identified.Comment: Proceedings of the 10th International Conference on Security and Privacy in Communication Networks (SecureComm 2014