Data mining

One Aggregated Approach in Multidisciplinary Based Modeling to Predict Further Students’ Education

In this paper, one multidisciplinary-applicable aggregated model has been proposed and verified. This model uses traditional techniques, on the one hand, and algorithms of machine learning as modern techniques, on the other hand, throughout the determination process of the relevance of model attributes for solving any problems of multicriteria decision. The main goal of this model is to take advantage of both approaches and lead to better results than when the techniques are used alone. In addition, the proposed model uses feature selection methodology to reduce the number of attributes, thus increasing the accuracy of the model. We have used the traditional method of regression analysis combined with the well-known mathematical method Analytic Hierarchy Process (AHP). This approach has been combined with the application of the ReliefF classificatory modern ranking method of machine learning. Last but not least, the decision tree classifier J48 has been used for aggregation purposes. Information on grades of the first-year graduate students at the Criminalistics and Police University, Belgrade, after they chose and finished one of the three possible study modules, was used for the evaluation of the proposed model. To the best knowledge of the authors, this work is the first work when considering mining closed frequent trees in case of the streaming of time-varying data.

Predictive Analytics and Data Mining

High dimensional databases are proving to be a major concern among the researches to extract relevant information for futuristic decision making. Real world data is high dimensional in nature and comprises of irrelevant features, missing values, and redundancy, which requires serious concerns. Utilizing all such features can mislead the results for emergent prediction. Therefore, such databases are critical in nature to determine optimal solutions. To deal with such issues, the authors have developed and implemented a Cluster Analysis Study Behavior of School Children from Large Databases (CABS) framework to retrieve effective and efficient clusters from high dimensional human behavior datasets for school children in US. They have applied feature selection technique and hierarchical agglomerative clustering technique to discover clusters of vivid shape and size to retrieve knowledge from large databases. This study was conducted for Health Behavior in School-Aged Children (HBSC) using Correlation-Based Feature Selection (CFS) technique to reduce the inconsistent data records and select relevant features that will eventually extract the appropriate data to merge similar data and retrieve clusters. However, predictive analytics can facilitate a more thorough extraction of knowledge to facilitate better quality and faster decisions. The authors have implemented the current framework in R language where the clustering was emphasized using pvclust package. The proposed framework is highly efficient in discovering hidden and implicit knowledge from large databases due to its accessibility to handling and discovering clusters of variant shapes.