Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth

Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal number of clusters has been challenging. Built upon our multiple imputation (MI) based fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation (MIV) framework and corresponding MIV algorithms for clustering big longitudinal eHealth data with missing values, more generally for fuzzy-logic based clustering methods. Specifically, we detect the optimal number of clusters by auto-searching and -synthesizing a suite of MI-based validation methods and indices, including conventional (bootstrap or cross-validation based) and emerging (modularity-based) validation indices for general clustering methods as well as the specific one (Xie and Beni) for fuzzy clustering. The MIV performance was demonstrated on a big longitudinal dataset from a real web-delivered trial and using simulation. The results indicate MI-based Xie and Beni index for fuzzy-clustering are more appropriate for detecting the optimal number of clusters for such complex data. The MIV concept and algorithms could be easily adapted to different types of clustering that could process big incomplete longitudinal trial data in eHealth services

EM Clustering Approach for Multi-Dimensional Analysis of Big Data Set

Data mining is one of the long known research topics, which is making a comeback especially with the advent of Big Data. ’Clustering’ technique is an important component in data mining. As we enter the Big Data era where many realworld datasets consist of multi-dimensional features, clustering has been gaining momentum in importance within this topic. The traditional clustering algorithms often fail to detect meaningful clusters in high-dimensional data set. Therefore, they become computationally expensive when dealing with data comprised of multiple dimensions. In this paper, we have proposed a modified technique that will perform well with high dimensional data set. In our proposed method we used Principle Component Analysis for dimension reduction before applying standard EM algorithm. The performance of the proposed set of algorithms is evaluated on the basis of silhouette index and time of execution