Mining time series data is an important approach for the analysis in many application areas as diverse as biology, environmental research, medicine, or stock chart analysis. As nearly all data mining tasks on this kind of data depend on a distance function between two time series, a huge number of such functions has been developed during the last decades. The introduction of threshold-based distance functions presented a new concept of time series similarity and these functions were applied to data mining techniques on a wide spectrum of time series data. In this demonstration, we present the Java toolkit T-Time which is able to perform several data mining tasks for a complete range of threshold values in an interactive way. The results are visually presented in a very concise way so that the user can easily identify important threshold values. Combined with domain-specific knowledge, these pivotal values can yield novel insights beyond the means of the underlying data mining techniques the analysis is based on.

In modern multimedia databases, objects can be specified by a large variety of feature representations. In this paper, we present a novel technique for multi-represented similarity estimation. We transform the distance between two objects in each representation into so-called similarity and dissimilarity estimates which are used to derive a meaningful similarity score. To determine the parameters for our new similarity measure, we present methods with and without user feedback.

Modeling objects by probability density functions (pdf) is a new powerful method to represent complex objects in databases. By representing an object as a pdf e.g. a Gaussian, it is possible to represent very large and complex objects in a compact and still descriptive way. In this contribution, we propose ProVeR a prototype search engine for content-based video retrieval which represents a video as a set of Gaussians. The Gaussians are managed by the Gauss-tree, an index structure allowing the efficient processing of probabilistic queries. ProVeR provides even non-expert users with an intuitive method for efficient, content-based retrieval of videos containing similar shots and scenes.

In this paper, we propose an approach for efficient approximative RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our method uses an approximation of the nearest-neighbor-distances in order to prune the search space. In several experiments, our solution scales significantly better than existing non-approximative approaches while producing an approximation of the true query result with a high recall.

Similarity search in time series data is an active area of research. In this paper, we introduce the novel concept of threshold-similarity queries in time series databases which report those time series exceeding a user-defined query threshold at similar time frames compared to the query time series. In addition, we present a new data structure to support threshold similarity queries efficiently. The performance of our solution is demonstrated by an extensive experimental evaluation.

In applications of biometric databases the typical task is to identify individuals according to features which are not exactly known. Reasons for this inexactness are varying measuring techniques or environmental circumstances. Since these circumstances are not necessarily the same when determining the features for different individuals, the exactness might strongly vary between the individuals as well as between the features. To identify individuals, similarity search on feature vectors is applicable, but even the use of adaptable distance measures is not capable to handle objects having an individual level of exactness. Therefore, we develop a comprehensive probabilistic theory in which uncertain observations are modeled by probabilistic feature vectors (pfv), i.e. feature vectors where the conventional feature values are replaced by Gaussian probability distribution functions. Each feature value of each object is complemented by a variance value indicating its uncertainty. We define two types of identification queries, k-mostlikely identification and threshold identification. For efficient query processing, we propose a novel index structure, the Gauss-tree. Our experimental evaluation demonstrates that pfv stored in a Gauss-tree significantly improve the result quality compared to traditional feature vectors. Additionally, we show that the Gauss-tree significantly speeds up query times compared to competitive methods.

Recently, more and more applications represent data objects as sets of feature vectors or multi-instance objects. In this paper, we propose COSMIC, a method for deriving concept lattices from multi-instance data based on hierarchical density-based clustering. The found concepts correspond to groups or clusters of multi-instance objects having similar instances in common. We demonstrate that COSMIC outperforms compared methods with respect to efficiency and cluster quality and is capable to extract interesting patterns in multi-instance data sets.

The rapid progress in digital music distribution has lead to the creation of large collections of music. There is a need for content-based music classification methods to organize these collections automatically using a given genre taxonomy. To provide a versatile description of the music content, several kinds of features like rhythm, pitch or timbre characteristics are commonly used. Taking the highly dynamic nature of music into account, each of these features should be calculated up to several hundreds of times per second. Thus, a piece of music is represented by a complex object given by several large sets of feature vectors. In this paper, we propose a novel approach for the hierarchical classification of music pieces into a genre taxonomy. Our approach is able to handle multiple characteristics of music content and achieves a high classification accuracy efficiently, as shown in our experiments performed on a real world data set

Similarity search in large multimedia databases is an important issue in nowadays multimedia environment. Multimedia objects such as music videos usually consist of multiple representations such as audio or video features. Since each representation may be of significantly different quality for a given multimedia object, similarity search methods could greatly benefit from taking these multiple representations into account. An intelligent similarity search technique should consider all available representations of the database objects and should automatically choose the best representations), i.e. those representations that model the object in the best possible way. In this paper, we propose a novel approach for similarity search in multimedia databases taking multiple representations of multimedia objects into account. In particular, we present weighting functions to rate the significance of a feature of each representation for a given database object. This allows weighting each representation during query processing. A broad experimental evaluation shows the suitability and the effectiveness of multi-represented similarity search in video databases

The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, is a generalization of the reverse 1-nearest neighbor problem which has received increasing attention recently. Many industrial and scientific applications call for solutions of the RkNN problem in arbitrary metric spaces where the data objects are not Euclidean and only a metric distance function is given for specifying object similarity. Usually, these applications need a solution for the generalized problem where the value of k is not known in advance and may change from query to query. However, existing approaches, except one, are designed for the specific R1NN problem. In addition - to the best of our knowledge - all previously proposed methods, especially the one for generalized RkNN search, are only applicable to Euclidean vector data but not for general metric objects. In this paper, we propose the first approach for efficient RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our approach uses the advantages of existing metric index structures but proposes to use conservative and progressive distance approximations in order to filter out true drops and true hits. In particular, we approximate the k-nearest neighbor distance for each data object by upper and lower bounds using two functions of only two parameters each. Thus, our method does not generate any considerable storage overhead. We show in a broad experimental evaluation on real-world data the scalability and the usability of our novel approach.

The issue of data mining in time series databases is of utmost importance for many practical applications and has attracted a lot of research in the past years. In this paper, we focus on the recently proposed concept of threshold similarity which compares the time series based on the time frames within which they exceed a user-defined amplitude threshold tau. We propose a novel approach for cluster analysis of time series based on adaptable threshold similarity. The most important issue in threshold similarity is the choice of the threshold tau. Thus, the threshold tau is automatically adapted to the characteristics of a small training dataset using the concept of support vector machines. Thus, the optimal tau is learned from a small training set in order to yield an accurate clustering of the entire time series database. In our experimental evaluation we demonstrate that our cluster analysis using adaptable threshold similarity can be successfully applied to many scientific real-world data mining applications

In many modern applications, there are no exact values available to describe the data objects. Instead, the feature values are considered to be uncertain. This uncertainty is modeled by probability distributions instead of exact feature values. A typical application of such an uncertainty model are moving objects where the exact position of each object can be determined only at discrete time intervals. Queries often involve the positions of objects between two such time stamps or after the last known time stamp. Then the objects are essentially uncertain unless the pattern of movement is very simple (e.g. linear). One of the most important probability density functions for those applications is the Gaussian or normal distribution which can be defined by a mean value and a standard deviation. In this paper, we examine a new type of queries on uncertain data objects, called probability ranking queries (PRQ). A PRQ retrieves those k objects which have the highest probability of being located inside a given query area. To speed up probabilistic queries on large sets of uncertain data objects described by Gaussians, we introduce a novel index structure called Gauss-tree. Furthermore, we provide an algorithm for employing the Gauss-tree to answer PRQs. In our experimental evaluation, we demonstrate that the Gauss-tree achieves a considerable efficiency advantage with respect to PRQs compared to other applicable methods

In many companies data is distributed among several sites, i.e. each site generates its own data and manages its own data repository. Analyzing and mining these distributed sources requires distributed data mining techniques to find global patterns representing the complete information. The transmission of the entire local data set is often unacceptable because of performance considerations, privacy and security aspects, and bandwidth constraints. Traditional data mining algorithms, demanding access to complete data, are not appropriate for distributed applications. Thus, there is a need for distributed data mining algorithms in order to analyze and discover new knowledge in distributed environments. One of the most important data mining tasks is clustering which aims at detecting groups of similar data objects. In this paper, we propose a distributed model-based clustering algorithm that uses EM for detecting local models in terms of mixtures of Gaussian distributions. We propose an efficient and effective algorithm for deriving and merging these local Gaussian distributions to generate a meaningful global model. In a broad experimental evaluation we show that our framework is scalable in a highly distributed environment.

Databases are a key technology for molecular biology which is a very data intensive discipline. Since molecular biological databases are rather heterogeneous, unification and data integration is mandatory to make use of the huge amount of available information. Currently, the most promising approach for integration is the use of ontologies. Since mapping biological entities into ontologies is usually achieved manually or semi-automatically, a system for automatic classification of biological entities into ontologies saves time and effort. Therefore, we present a support vector machine based approach that automatically classifies biological entities into a given ontology. To solve this difficult task, our method copes with the following aspects. Biological entities might belong to more than one class or may be placed in classes on varying abstraction levels. An object may be described by several representations. Thus, the classifier has to be enabled to draw information from all of them, but must consider the possibility that some objects are described incompletely. Therefore, our method introduces the technique of object-adjusted weighting which regulates the impact of each representation dynamically for each object. To significantly improve the time performance of the classifier we exploit the inheritance relations of the given ontology. Our experimental evaluation on protein data and several parts of an established molecular biological ontology shows that our prototype offers impressive accuracy and is efficient enough to cope with the large number of classes encountered in real world problems.