Predictive Analytics and Data Mining

Abstract

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.