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Abstract Event detection over microblogs has attracted great research interest due to its wide application in crisis management and decision making etc. In natural disasters, complex events are reported in real time on social media sites, but these reports are invisible to crisis coordinators. Detecting these crisis events helps watchers to make right decisions rapidly, reducing injuries, deaths and economic loss. In sporting activities, detecting events helps audiences make better and more timely game viewing plans. However, existing event detection techniques are not effective at handling complex social events that evolve over time. In this paper, we propose an event detection method that takes advantage of retweeting behavior for handling the events evolution. Specifically, we first propose a topic model called RL-LDA to capture the social media information over hashtag, location, textual and retweeting behavior. Using RL-LDA, a complex event can be well handled by exploring the correlation between retweeting behavior and the event. Then to maintain the RL-LDA in a dynamic environment, we propose a dynamic update algorithm, which incrementally updates events over real time streams. Experiments over real-world datasets show that RL-LDA detects the temporal evolution of complex events effectively and efficiently. [ABSTRACT FROM AUTHOR]

This paper investigates how social images and change detection techniques can be applied to identify the damage caused by natural disasters for disaster assessment. We propose a multi-step image matching-based framework (MSIM), which takes advantages of fast clustering, near duplicate image detection and robust boundary matching for the change detection in disasters. We first model the social images by exploiting their tags and locations. After that, we propose a recursive 2-means algorithm over the new data model, and refine the near duplicate detection by local interest point-based matching over image pairs in neighboring clusters. Finally, we propose a novel boundary representation model called relative position annulus (RPA), which is robust to boundary rotation, location shift and editing operations. A new RPA matching method is proposed by extending dynamic time wrapping (DTW) from time to position annulus. We have done extensive experiments to evaluate the high effectiveness and efficiency of our approach. [ABSTRACT FROM AUTHOR]

Hierarchical clustering is of great importance in data analytics especially because of the exponential growth of real-world data. Often these data are unlabelled and there is little prior domain knowledge available. One challenge in handling these huge data collections is the computational cost. In this paper, we aim to improve the efficiency by introducing a set of methods of agglomerative hierarchical clustering. Instead of building cluster hierarchies based on raw data points, our approach builds a hierarchy based on a group of centroids. These centroids represent a group of adjacent points in the data space. By this approach, feature extraction or dimensionality reduction is not required. To evaluate our approach, we have conducted a comprehensive experimental study. We tested the approach with different clustering methods (i.e., UPGMA and SLINK), data distributions, (i.e., normal and uniform), and distance measures (i.e., Euclidean and Canberra). The experimental results indicate that, using the centroid based approach, computational cost can be significantly reduced without compromising the clustering performance. The performance of this approach is relatively consistent regardless the variation of the settings, i.e., clustering methods, data distributions, and distance measures. [ABSTRACT FROM AUTHOR]

• Clustering is used to find groups of similar instances. • Multi-view feature can further benefit the instance selection process. • Results show the effectiveness and efficiency of the proposed model. Multi-instance learning (MIL) is a special kind of classification problem where samples (called "instances") are grouped into bags and labels are given only on bag level instead of instance level. From the expert and intelligent system perspective, MIL does not require full ground-truth labels which helps to reduce the cost of data labeling in real tasks. Many inviting problems such as image classification, video annotation and object detection can be formulated in MIL frameworks. In this paper, we propose a similarity-based method with clustering in a multi-view feature manner to solve MIL problems efficiently. The clustering is introduced as a novel strategy for instance selection. Considering unlabeled data, clustering methods fit well for obtaining the hidden structure information in feature space, and thus we utilize a clustering-based strategy to exploit such information on discovering positive instances. To fully use the original input data, we further develop our method in multi-view feature space, in order to make our model capture information from different feature space and provide support for the whole system. Experiments on benchmark datasets for classification have been conducted, and we also perform image retrieval to examine the extended multi-view model on a large image dataset, MIL-NUS-WIDE. The promising results demonstrate the effectiveness of our clustering-based MIL (CMIL) model. [ABSTRACT FROM AUTHOR]