We present a novel clustering approach for moving object trajectories that are constrained by an underlying road network. The approach builds a similarity graph based on these trajectories then uses modularity-optimization hiearchical graph clustering to regroup trajectories with similar profiles. Our experimental study shows the superiority of the proposed approach over classic hierarchical cl...

In most cases where clustering of data is desirable, the underlying data distribution to be clustered is unconstrained. However clustering of site types in a discretely structured linear array, as is often desired in studies of linear sequences such as DNA, RNA or proteins, represents a problem where data points are not necessarily exchangeable and are directionally constrained within the array...

Data clustering is the concept of forming predefined number of clusters where the data points within each cluster are very similar to each other and the data points between clusters are dissimilar to each other. The concept of clustering is widely used in various domains like bioinformatics, medical data, imaging, marketing study and crime analysis. The popular types of clustering techniques ar...

We consider the problem of clustering under the constraint that data points in the same cluster are connected according to a pre-existed graph. This constraint can be efficiently addressed by an agglomerative clustering approach, which we exploit to construct a new fully automatic segmentation algorithm for color photographs. For image segmentation, if the pixel grid with eight neighbor connect...

Capturing application semantics and allowing a human analyst to express his focus in mining have been the motivation for several recent studies on constrained mining. In this paper, we introduce and study the problem of constrained clustering| nding clusters that satisfy certain user-speci ed constraints. We argue that this problem arises naturally in practice. Two types of constraints are disc...

We introduce our participation of the TREC Relevance Feedback(RF) TRACK in 2009. The RF09 TRACK is focused on the explicit relevant feedback, where a few relevant and irrelevant documents are available to each query. Our system is implemented under the framework of probabilistic language model. We apply the constrained clustering on the top returned documents and extract the expanded words to r...

MOTIVATION Gene expression profiles should be useful in distinguishing variations in disease, since they reflect accurately the status of cells. The primary clustering of gene expression reveals the genotypes that are responsible for the proximity of members within each cluster, while further clustering elucidates the pathological features of the individual members of each cluster. However, sin...

Unsupervised image clustering is a challenging and often illposed problem. Existing image descriptors fail to capture the clustering criterion well, and more importantly, the criterion itself may depend on (unknown) user preferences. Semi-supervised approaches such as distance metric learning and constrained clustering thus leverage user-provided annotations indicating which pairs of images bel...