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Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant subfield in knowledge management. A classification of the data mining methods would greatly simplify the understanding of the whole space of available methods. Decision tree learning algorithm has been successfully used in expert systems in capturing knowledge. Most decision tree classifiers are designed to classify the data with categorical or Boolean class labels. To the best of our knowledge, no previous research has considered the induction of decision trees from data with data dissimilarities. This work proposes a novel classification algorithm for learning decision tree classifiers from data using dissimilarities with less complexity and less time to construct decision tree.