Statistical Mining Techniques Research Articles

A Statistical-Mining Techniques’ Collaboration for Minimizing Dimensionality in Ovarian Cancer Data

A feature is a single measurable criterion to an observation of a process. While knowledge discovery techniques successfully contribute to many fields, however, the extensive required data processing could hinder the performance of these techniques. One of the main issues in processing data is the dimensionality of the data. Therefore, focusing on reducing the data dimensionality through eliminating the insignificant attributes could be considered one of the successful steps for raising the applied techniques’ performance. On the other hand, focusing on the applied field, ovarian cancer patients continuously suffer from the extensive analysis requirements for detecting the disease as well as monitoring the treatment progress. Therefore, identifying the most significant required analysis could be a positive step to reduce the emotional and financial suffering. This research aims to reduce the data dimensionality of the ovarian cancer disease and highlight the most significant analysis using the collaboration of clustering techniques and statistical techniques. The research succeeded to identify twelve significant analysis out of forty-four with a total of fourteen significant attributes for ovarian cancer data.

Bottom-up model for the sustainability assessment of rooftop-farming technologies potential in schools in Quito, Ecuador

Rooftop-farming technologies can transform unexploited roofs into agricultural areas; and though studies have quantified the sustainability of diverse rooftop-farm systems, researchers lack a direct comparison between these farms using a unified sustainability index. Therefore the proposed bottom-up model aims to quantify the sustainability of rooftop-farm technologies application in school building stocks, thus permitting an objective comparison and ultimately selection of the best-fitted farm system. This model handles large building samples by combining Statistical Mining Techniques with the Integrated Value Model for Sustainability Assessment. It uses data on the economic, environmental and social aspects of the farms, and relates them to the technical limitations and functionality found in the host buildings. The model has three consecutive stages: 1) in the City Stage reference buildings are identified from the stock, 2) the Building Information Stage determines the logistics and infrastructure requirements; and, 3) the Farm Technology Stage quantifies the farms’ sustainability. This model was used to assess the potential implementation of three rooftop-farms (edible-green roofs, rooftop greenhouses and integrated rooftop greenhouses) in the primary school stock in Quito, Ecuador. Two reference buildings represented the primary school stock of the city; and, in both typologies, edible-green roofs obtained the highest sustainability values of 0.62 and 0.65. The environmental pillar was the most discriminant in which green-roofs achieved twice the sustainability values for the rooftop-greenhouses due to their larger rainwater harvesting capacity, thermal resistance and contribution to the increment of urban greenspaces.

Multivariate adaptive regression splines and insider trading data for stock prediction

Data mining statistical techniques have not been used to improve the prediction of abnormal stock returns using insider trading data (except for one preliminary study by the author using neural networks). To expand data mining statistical research in this area, an investigation using Multivariate Adaptive Regression Splines (MARS) was initiated. The investigated covered 343 companies for a period of 4.5 years. Study findings revealed that the prediction of abnormal returns could be enhanced in the following ways : (1) extending the time of the future forecast up to 1 year ; (2) increasing the period of back aggregated data up to 4 months ; and (3) narrowing the assessment to certain industries such as electronic equipment and business services.