Employee Attrition Probability k – Means Clustering
This node outputs the cluster centers for a predefined number of clusters (no dynamic number of clusters). K-means performs a crisp clustering that assigns a data vector to exactly one cluster. The algorithm terminates when the cluster assignments do not change anymore.
The clustering algorithm uses the Euclidean distance on the selected attributes. The data is not normalized by the node (if required, you should consider using the “Normalizer” as a preprocessing step).
Critical Areas of HR Predictive Analytics
Employee Profiling and Segmentation
Predictive analytics can be leveraged for effective talent management by accurately profiling and segmenting employees.
Segmenting the existing employee base can help management understand the workforce better.
The lessons from this segmentation process can be applied to effectively classifying employees in the future.
Employee data such as demographics, skills, educational background, experience, and designation can be combined with information on roles and responsibilities to create such segments.
Companies can achieve higher employee satisfaction score and better relationship with employees by selecting relevant programs for segments that are likely to benefit the most from these initiatives in the future.