Making investment decision on various available stocks in the market is a challenging task. Econometric and statistical models, as well as machine learning and data mining techniques, have proposed heuristic based solutions with limited long-range success. In practice, the capabilities and intelligence of financial experts is required to build a managed portfolio of stocks. However, for non-professional investors, it is too complicated to make subjective judgments on available stocks and thus they might be interested to follow an expert's investment decision. For this purpose, it is critical to find an expert with similar investment preferences. In this work, we propose to benefit from the power of Social Network Analysis in this domain. We first build a social network of financial experts based on their publicly available portfolios. This social network is then used for further analysis to recommend an appropriate managed portfolio to non-professional investors based on their behavioral similarities to the expert investors. This approach is evaluated through a case study on real portfolios. The result shows that the proposed portfolio recommendation approach works well in terms of Sharpe ratio as the portfolio performance metric.

The user-centric query interface is very common application that allows expressing both the input and the output using fuzzy terms. This is becoming a need in the evolving internet-based era where web-based applications are very common and the number of users accessing structured databases is increasing rapidly. Restricting the user group to only experts in query coding must be avoided. The Ask Fuzzy system has been developed to address this vital issue which has social and industrial impact. It is an attractive and friendly visual user interface that facilitates expressing queries using both fuzziness and traditional methods. The fuzziness is not expressed explicitly inside the database, it is rather absorbed and effectively handled by an intermediate layer which is cleverly incorporated between the front-end visual user-interface and the back-end database.

This paper addresses XML document classification by considering both structural and content-based features of the documents. This approach leads to better constructing a set of informative feature vectors that represents both structural and textual aspects of XML documents. For this purpose, we integrate soft clustering of words and feature reduction into the process. To extract structural information, we employ an existing frequent tree-mining algorithm combined with an information gain filter to retrieve the most informative substructures from XML documents. However, for extracting content information, we propose soft clustering of words using each cluster as a textual feature. We have conducted extensive experiments on a benchmark dataset, namely 20NewsGroups, and an XML documents dataset given in LOGML that describes the web-server logs of user sessions. With regards to the classifier built only using our textual features, the results show that it outperforms a naive support-vector-machine (SVM)-based classifier, as well as an information retrieval classifier (IRC). We further demonstrate the effectiveness of incorporating both structural and content information into the process of learning, by comparing our classifier model and several XML document classifiers. In particular, by applying SVM and decision tree algorithms using our feature vector representation of XML documents dataset, we have achieved 85.79% and 87.04% classification accuracy, respectively, which are higher than accuracy achieved by XRules, a well-known structural-based XML document classifier.

In this paper, a new framework to build an adaptive classifier is introduced. At first, a clustering algorithm, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is applied to a set of sample data to form initial set of clusters. The clusters are represented as classes. Using support vector machine (SVM), a classifier model is generated. In real world application, data comes in continuously. Therefore, if the model does not learn from the new data, the model may not perform as well with the new data especially when the model's training data is different from the test data. The new framework proposed in this paper rebuilds the classifier model using selected data from test data set to improve the accuracy of the model. A case study on intrusion detection data set has been performed to evaluate our methodology. The result shows that this approach lead to have more accurate classification models over time.

In this paper, we investigate and explore the process of analyzing log data of website visitor traffic in order to assist the owner of a website in understanding the behavior of its visitors. The developed approach involves the review of statistical data on the types of visitors that come to the website, as well as the steps they take to reach and satisfy the goal of their visit. The value added from the analysis of this data provides eCommerce and commercial website owners with the information needed to display targeted advertisements or messages to their customers. In the long run, this is expected to allow for the increase in sales and overall customer loyalty.

Automated text summarization is important to for humans to better manage the massive information explosion. Several machine learning approaches could be successfully used to handle the problem. This paper reports the results of our study to compare the performance between neural networks and support vector machines for text summarization. Both models have the ability to discover non-linear data and are effective model when dealing with large datasets.

The great increase in online transactions and the thousands of online retailers has created a great demand for companies to gain competitive advantage. An easy way for a company to gain customer advantage is through the use of data mining. Due to this high demand we have developed a prototypical tool to help with the analysis of these online transactions. From the raw data generated by running these transactions we are able to find consumer trends and shopping patterns by using hierarchical clustering and association rules mining algorithm. The focus of this research is to demonstrate how this development can be useful and effective in a business situation for companies to gain competitive advantage.

This paper addresses the development of a plug and run wrapper to incorporate fuzziness into VRXQuery, the querying facility of VIREX which is a user-friendly system for transforming and querying relational data as XML. Our basic argument is not to f

Geo-data sets are built for use in geographical information systems (GIS). The data is modeled to suit the needs of data entry and visual representations. They are optimized for simplicity and speed of modification. These models do not lend themselves to efficiently produce enterprise reports. Hence, Geo-data sets can be very challenging to query and analyze. In this paper, we create a data warehouse (DW) for a geo-dataset to facilitate report generating processes. A data warehouse is attractive as the main repository of an organization's historical data and is optimized for reporting and analysis. For better understanding of the developed architecture, we cover the data warehouse construction process as well as the enterprise data model

In this paper, we demonstrate how cluster analysis can be used to effectively identify communities using information derived from the Call Detail Record (CDR) data. We use the information extracted from the cluster analysis to identify customer calling patterns. Customers calling patterns are then given to a classification algorithm to generate a classifier model for predicting the calling communities of a customer. We apply two different classification methods: Support vector machine and fuzzy-genetic classifier. The latter method is used for possibly assigning a customer to different classes with different degrees of membership. The reported test results demonstrate the applicability and effectiveness of the proposed approach.

This paper addresses the development of a plug and run wrapper to incorporate fuzziness into VRXQuery, the querying facility of VIREX which is a user-friendly system for transforming and querying relational data as XML. Our basic argument is not to force the underlying XML data to incorporate fuzziness. Rather, fuzziness is smoothly supported in a novel plug and run manner via a wrapper. Either the user specifies the membership functions for the elements/attributes to be queried as fuzzy, or multi-objective genetic algorithm is used to automatically decide on and optimize the membership functions. The interface of VIREX has been expanded to allow specifying queries with fuzziness. Then, queries expressed in VRXQuery empowered with fuzziness are translated into corresponding XQuery code, which is run on the underlying XML and the returned result is translated into a fuzzy representation; translation into SQL is also possible. The user is given the choice to display the result either as colored- text or in graphical format.

Location prediction is a special case of spatial data mining classification. For instance, in the public safety domain, it may be interesting to predict location(s) of crime hot spots. In this study, we present Support Vector Machine (SVM) based approach to predict the location as alternative to existing modeling approaches. SVM forms the new generation of machine learning techniques used to find optimal separability between classes within datasets. Experiments on two different spatial datasets show that SVMs gives reasonable results.