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One of the most used techniques among many others in the data mining field is the clustering. The aim of this technique is to synthetize and summarize huge amounts of data by splitting it into small and homogenous clusters such that the data (observations) inside the same cluster are more similar to each other than to the observations inside the other clusters. This definition assumes that there exists a well defined clustering quality measure that quantifies how much homogeneous are the obtained clusters. The aim of this chapter is to expose an original approach to merge different partitions, related to the same data set, which are obtained either by applying different clustering techniques either by the same clustering technique with different parameters. Fusing partitions has been broadly studied and has been given several names, depending on different scientific fields, like machine learning or bioinformatics (Dudoit & Fridlyand, 2003; Kim & Lee, 2007; Monti et al., 2003). Among these names we can quote: consensus clustering, clustering aggregation, clustering combination, fusion of clustering, ...etc. Several studies (Frossyniotis et al., 2002; Minaei-Bidgoli et al., 2004; Strehl & Ghosh, 2002; Topchy et al., 2004; 2005) have pioneered clustering data sets as a new branch of the conventional clustering methodology. In (Topchy et al., 2004) the authors propose a probabilistic formalism of clustering concensus using a finite mixture of multinomial distributions in a space of clustering. The approach proposed in (Frossyniotis et al., 2002) is designed for combining runs of clustering algorithms with the same number of clusters. In (Strehl & Ghosh, 2002) the authors proposed combiners based on a hyper-graph model to solve the cluster fusion problem. The authors discuss two manners of consensus clustering: (1) Feature Distributed Clustering (FDC): a set of clustering are obtained from partial view of variables using all observations (2) Object-Distributed Clustering (ODC): with this technique the ensemble clustering has limited to subset of observation with access to all variables. The 3

SPIE EUROPE SECURITY + DEFENCE , Toulouse, France, 21-/09/2015 - 24/09/2015; Public transport security is one of the main priorities of the public authorities when fighting against crimes and terrorism. In this context, there is a great demand for autonomous systems able to detect abnormal events such as violent acts aboard coaches and intrusions when the train is parked at the depot. To this end, we present an innovative approach which aims at providing efficient automatic event detection by fusing video and audio analytics and reduce the false alarm rate compared to classical video detection. The multi-modal system is composed of two microphones and one camera and integrates onboard video and audio analytics and fusion capabilities. On the one hand, for detecting intrusion, the system relies on the fusion of 'unusual' audio events detection with intrusion detections from video processing. The audio analysis consists in modeling the normal ambience, and detecting deviation from the trained models during testing. This unsupervised approach is based on clustering of automatically extracted segments of acoustic features and statistical GMM modeling of each cluster. The intrusion detection is based on the 3D detection and tracking of individuals in the videos. On the other hand, for violent events detection, the system fuses unsupervised and supervised audio algorithms with video event detection. The supervised audio technique detects specific events such as shouts. A Gaussian Mixture Model is used to catch the formantic structure of a shout signal. Video analytics use an original approach for detecting aggressive motion by focusing on erratic motion patterns specific to violent events. As data with violent events is not easily available, a normality model with structured motions from non-violent videos is learned for one-class classification. A fusion algorithm based on Dempster-Shafer's theory analyses the asynchronous detection outputs and computes the degree of belief of each probable event.

International audience; In this chapter, we first present the state of the art in the domain. After we discuss how we achieve the pre-processing of the data. Then activity analysis and automatic classification is presented and finally we provide some results and evaluations.; Dans ce chapitre, nous faisons un état de l'art. Ensuite nous montrons comment nous faisons le pre processind des data. Cette phase nous permet da faire l'analyse des activités et leur classification automatique. Après, quelques résultats et des évaluations sont présentés.

International audience; Extracting the hidden and useful knowledge embedded within video sequences and thereby discovering relations between the various elements to help an efficient decision-making process is a challenging task. The task of knowledge discovery and information analysis is possible because of recent advancements in object detection and tracking. The authors present how video information is processed with the ultimate aim to achieve knowledge discovery of people activity and also extract the relationship between the people and contextual objects in the scene. First, the object of interest and its semantic characteristics are derived in real-time. The semantic information related to the objects is represented in a suitable format for knowledge discovery. Next, two clustering processes are applied to derive the knowledge from the video data. Agglomerative hierarchical clustering is used to find the main trajectory patterns of people and relational analysis clustering is employed to extract the relationship between people, contextual objects and events. Finally, the authors evaluate the proposed activity extraction model using real video sequences from underground metro networks (CARETAKER) and a building hall (CAVIAR).

This paper deals with the problem of combining multiple clustering algorithms using the same data set to get a single consensus clustering. Our contribution is to formally define the cluster consensus problem as an optimization problem. to reach this goal, we propose an original existing algorithm but still relatively unknown method named relational analysis (RA). This method has several advantages among which we can quote: its low computational complexity, it does not require a number of clusters and does not neglect the weak clustering result. The unsupervised clustering consensus method implemented in this work is quite general. We evaluate the effectiveness of cluster consensus in three qualitatively different data sets. Promising results are provided in all three situations for synthetic as well as real data sets.