Decision trees have been successfully used for the task of classification. However, state-of-the-art algorithms do not incorporate the user in the tree construction process. This paper presents a new user-centered approach to this process where the user and the computer can both contribute their strengths: the user provides domain knowledge and evaluates intermediate results of the algorithm, the computer automatically creates patterns satisfying user constraints and generates appropriate visualizations of these patterns. In this cooperative approach, domain knowledge of the user can direct the search of the algorithm. Additionally, by providing adequate data and knowledge visualizations, the pattern recognition capabilities of the human can be used to increase the effectivity of decision tree construction. Furthermore, the user gets a deeper understanding of the decision tree than just obtaining it as a result of an algorithm. To achieve the intended level of cooperation, we introduce a new visualization of data with categorical and numerical attributes. A novel technique for visualizing decision trees is presented which provides deep insights into the process of decision tree construction. As a key contribution, we integrate a state-of-the-art algorithm for decision tree construction such that many different styles of cooperation- ranging from completely manual over combined to completely automatic classification- are supported. An experimental performance evaluation demonstrates that our cooperative approach yields an efficient construction of decision trees that have a small size, but a high accuracy. 1.

Article Free Access Share on Visual classification: an interactive approach to decision tree construction Authors: Mihael Ankerst Institute for Computer Science, University of Munich, Oettingenstr. 67, D-80538 München, Germany Institute for Computer Science, University of Munich, Oettingenstr. 67, D-80538 München, GermanyView Profile , Christian Elsen Institute for Computer Science, University of Munich, Oettingenstr. 67, D-80538 München, Germany Institute for Computer Science, University of Munich, Oettingenstr. 67, D-80538 München, GermanyView Profile , Martin Ester Institute for Computer Science, University of Munich, Oettingenstr. 67, D-80538 München, Germany Institute for Computer Science, University of Munich, Oettingenstr. 67, D-80538 München, GermanyView Profile , Hans-Peter Kriegel Institute for Computer Science, University of Munich, Oettingenstr. 67, D-80538 München, Germany Institute for Computer Science, University of Munich, Oettingenstr. 67, D-80538 München, GermanyView Profile Authors Info & Claims KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 1999 Pages 392–396https://doi.org/10.1145/312129.312298Published:01 August 1999Publication History 108citation1,861DownloadsMetricsTotal Citations108Total Downloads1,861Last 12 Months173Last 6 weeks20 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a preprocessing step for other algorithms operating on the detected clusters. Almost all of the well-known clustering algorithms require input parameters which are hard to determine but have a significant influence on the clustering result. Furthermore, for many real-data sets there does not even exist a global parameter setting for which the result of the clustering algorithm describes the intrinsic clustering structure accurately. We introduce a new algorithm for the purpose of cluster analysis which does not produce a clustering of a data set explicitly; but instead creates an augmented ordering of the database representing its density-based clustering structure. This cluster-ordering contains information which is equivalent to the density-based clusterings corresponding to a broad range of parameter settings. It is a versatile basis for both automatic and interactive cluster analysis. We show how to automatically and efficiently extract not only 'traditional' clustering information (e.g. representative points, arbitrary shaped clusters), but also the intrinsic clustering structure. For medium sized data sets, the cluster-ordering can be represented graphically and for very large data sets, we introduce an appropriate visualization technique. Both are suitable for interactive exploration of the intrinsic clustering structure offering additional insights into the distribution and correlation of the data.

Shape similarity searching is a crucial task in image databases, particularly in the presence of errors induced by segmentation or scanning images. The resulting slight displacements or rotations have not been considered so far in the literature. We present a new similarity model that flexibly addresses this problem. By specifying neighborhood influence weights, the user may adapt the similarity distance functions to his or her own requirements or preferences. Technically, the new similarity model is based on quadratic forms for which we present a multi-step query processing architecture, particularly for high dimensions as they occur in image databases. Our algorithm to reduce the dimensionality of quadratic form-based similarity queries results in a lower-bounding distance function that is proven to provide an optimal filter selectivity. Experiments on our test database of 10,000 images demonstrate the applicability and the performance of our approach, even in dimensions as high as 1,024.

An important goal of visualization technology is to support the exploration and analysis of very large amounts of data. In this paper, we propose a new visualization technique called a 'recursive pattern', which has been developed for visualizing large amounts of multidimensional data. The technique is based on a generic recursive scheme which generalizes a wide range of pixel-oriented arrangements for displaying large data sets. By instantiating the technique with adequate data- and application-dependent parameters, the user may greatly influence the structure of the resulting visualizations. Since the technique uses one pixel for presenting each data value, the amount of data which can be displayed is only limited by the resolution of current display technology and by the limitations of human perceptibility. Beside describing the basic idea of the 'recursive pattern' technique, we provide several examples of useful parameter settings for the various recursion levels. We further show that our 'recursive pattern' technique is particularly advantageous for the large class of data sets which have a natural order according to one dimension (e.g. time series data). We demonstrate the usefulness of our technique by using a stock market application.