Liu, Fengmei, Gupta, Sucharu and Tortora, Cristina - Archives of Data Science, Series B

Article Details

Title Cluster Correspondence Analysis and Reduced K-Means: A Two-Step Approach to Cluster Low Back Pain Patients
Authors Liu, Fengmei, Gupta, Sucharu and Tortora, Cristina
Year 2019
Volume 1(1)
Abstract For the IFCS 2017 data challenge on low back pain (LBP) patients clustering, we used a two-step approach. Two of the challenging characteristics of the data set are the presence of missing values and mixed type variables. After a specific pretreatment, in the first step, we performed domain clustering using cluster correspondence analysis (clusCA). Upon the output variables from each domain, we did the second step, reduced K-means clustering, to get the final clusters of patients. The conclusion section shows the final clustering results and a profile plot of the clusters. Every cluster is highly interpretable and evaluated well with some descriptive variables which are used for measuring the clustering results.