Clustering Auto-Fluorescence Spectrogram Data for Colorectal Carcinoma

Hybrid data clustering analysis is an important issue in data mining. After analyzing the traditional clustering algorithms, the paper presents a new algorithm to cluster colorectal carcinoma auto-fluorescence spectrogram data based on lattice after analyzing the characteristics of biomedicine data. The method changes the objectpsilas attributes to lattice based on the conception of simple tuples and hyper tuples in lattice, uses the numbers of covers to measure the similarity between labels, and chooses the clustering mean-point according to the rule of high covers to high similarity. Experiments show that the new algorithm is more efficiently than the other classical ones. More importantly it is a method that works for ordinal, nominal or mixed data.