| 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 | Archives of Data Science, Series B 1(1) / 2019 |
| 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. |