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This paper presents a new framework for simple, interactive volume exploration of biological datasets. We accomplish this by automatically creating dataset-specific transfer functions and utilizing them during direct volume rendering. The proposed method employs a K-Means++ clustering algorithm to classify a two-dimensional histogram created from the input volume. The classification process utilizes spatial and data properties from the volume. Then using properties derived from the classified clusters, our method automatically generates color and opacity transfer functions and presents the user with a high quality initial rendering of the volume data. Our method estimates classification parameters automatically, yet users are also allowed to input or override parameters to utilize pre-existing knowledge of their input data. User input is incorporated through the simple yet intuitive interface for transfer function manipulation included in our framework. Our new interface helps users focus on feature space exploration instead of the usual effort intensive, low-level widget manipulation. We evaluated the framework using three-dimensional medical and biological images. Our preliminary results demonstrate the effectiveness of our method of automating transfer function generation for high quality initial visualization. The proposed approach effectively generates automatic transfer functions and enables users to explore and interact with their data in an intuitive way, without requiring detailed knowledge of computer graphics or rendering techniques. Funded by NCI Contract No. HHSN261200800001E.