Proliferation of touch-based devices has made sketch-based image retrieval practical. While many methods exist for sketch-based object detection/image retrieval on small datasets, relatively less work has been done on large (web)-scale image retrieval. In this paper, we present an efficient approach for image retrieval from millions of images based on user-drawn sketches. Unlike existing method...

In many data mining applications the data objects are modeled as sets of feature vectors or multi-instance objects. In this paper, we present an expectation maximization approach for clustering multiinstance objects. We therefore present a statistical process that models multi-instance objects. Furthermore, we present M-steps and E-steps for EM clustering and a method for finding a good initial...

Fractal dimension is an index which can be used to characterize urban areas. The use of the curve of scaling behaviour is less common. However, its shape gives local information about the morphology of the built-up area. This paper suggests a method based on a k-medoid for clustering these curves. It is applied to forty-nine wards of European cities, and shows that the curves add interesting in...

This paper investigates the text categorization capabilities of two special clustering algorithms: Fuzzy k-Medoid and MAJORCLUST. Aside from quantifying the categorization performance of the mentioned algorithms, our experimental setting will also help to answer special questions related to clustering problems such as cluster number determination or cluster quality evaluation.

Distance metrics are often the backbone of clustering algorithms. Yet certain distance metrics, such as one based on Pearson’s correlation, are sensitive to outliers. Microarray data tend to have outlying data points. Hence, we may intuitively believe metrics like one based on Pearson’s correlation may not be appropriate for clustering microarray data. Hardin, et al. (2007) show a metric based ...

Despite outstanding successes of the state-of-the-art clustering algorithms, many of them still suffer from shortcomings. Mainly, these algorithms do not capture coherency and homogeneity of clusters simultaneously. We show that some of the best performing spectral as well as hierarchical clustering algorithms can lead to incorrect clustering when the data is comprised of clusters with differen...