A conditionally positive definite kernel function for clustering of incomplete data

Clustering of incomplete data sets that contains missing features is one of the most widely studied problems in the literature, and several imputation and non-imputation techniques are used to solve this problem. A weighted sum of the Euclidean distance from the datum to the corresponding clusters is used in Fuzzy c-means clustering. It has been observed that the kernel-based clustering techniques outperform the conventional algorithms in terms of accuracy. This is due to their ability to handle non-linear data and map it to higher dimensional space while preserving its internal structure. Kernel functions are really important when it comes to the performance of kernel-based clustering methods. Choosing the right kernel function isn't simple. Among the various clustering algorithms that have been examined in the literature, the, Gaussian kernel function has been found to be more useful.. This paper suggests a conditionally positive definite kernel function that can be used in the unsupervised clustering of incomplete data. Numerical analysis shows that the conditionally positive definite kernel function also performs well on datasets with incomplete features.