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Social tagging in online communities has become an important method for reflecting classified thoughts of individual users. A number of social Web sites provide tagging functionalities and also offer folksonomies within or across the sites. However, it is practically not easy to find users' interests based on such folksonomies. In this paper, we provide a novel approach for clustering user-centric...
The growth of the web has directly influenced the increase in the availability of relational data. One of the key problems in mining such data is computing the similarity between objects with heterogeneous feature types. For example, publications have many heterogeneous features like text, citations, authorship information, venue information, etc. In most approaches, similarity is estimated using...
The concept of Triclusters has been investigated recently in the context of two relational datasets that share labels along one of the dimensions. By simultaneously processing two datasets to unveil triclusters, new useful knowledge and insights can be obtained. However, some recently reported methods are either closely linked to specific problems or constrain datasets to have some specific distributions...
The acquisition of new scientific knowledge and the evolution of the needs of the society regularly call into question the orientations of research. Means to recall and visualize these evolutions are thus necessary. The existing tools for research survey give only one fixed vision of the research activity, which does not allow performing tasks of dynamic topic mining. The objective of this paper is...
Data mining algorithms have been proved to be useful for the processing of large data sets in order to extract relevant information and knowledge. Such algorithms are also important for analyzing data collected from the users' activity users. One family of such data analysis is that of mining of log files of online applications that register the actions of online users during long periods of time...
Recently, the number of freely available online reviews is increasing in a high speed. More and more aspect based opinion mining technique has been employed to find out customers' opinions. In this paper, we only focus on categorize product features that the customers have commented on. An unsupervised twice-clustering based product features categorization method is proposed. Opinion words in context...
We propose a novel method for fusing different classifiers outputs. Our approach, called Context Extraction for Local Fusion with Fuzzy Integrals (CELF-FI), is a local approach that adapts fuzzy integrals fusion method to different regions of the feature space. It is based on a novel objective function that combines context identification and multi-algorithm fusion criteria into a joint objective...
We propose interest seam image, an efficient visual synopsis for video. To extract an interest seam image, a spatiotemporal energy map is constructed for the target video shot. Then an optimal seam which encompasses the highest energy is identified by an efficient dynamic programming algorithm. The optimal seam is used to extract a seam of pixels from each video frame to form one column of an image,...
This paper introduces a novel incremental approach to clustering uncertain categorical data. This so-called Incremental K Belief K-modes Method (IK-BKM) extends the Belief K-modes one to update the cluster partition when new information is available namely the increase of final desired clusters' number. The main objective is to update clusters' partition without complete reclustring. Our method will...
This paper evaluates CONSPECT, a service that analyses states in a learner's conceptual development. It combines two technologies - Latent Semantic Analysis to analyse text and Network Analysis (NA) to provide visualisations - into a technique called Meaningful Interaction Analysis (MIA). CONSPECT was designed to help both online learners and their tutors monitor their conceptual development. This...
This paper presents a system that combines two text mining techniques; information extraction and clustering. A rule-based approach is used to perform the information extraction task, based on the dependency relation between some intransitive verbs and prepositions. This relationship helps in extracting types of crime from documents within the crime domain. With regard to the clustering task, the...
The development of mobile network technology provides a great potential for social networking services. This paper studied data mining for social network analysis purpose, which aims at find people's social network patterns by analyzing the information about their mobile phone usage. In this research, the real database of MIT's Reality Mining project is employed. The classification model presented...
In this paper we propose a map matching method to overcoming the limitations of standard best-match reconstruction strategies. We use a more flexible approach which consider the k-optimal alternative paths to reconstruct the trajectories from the GPS raw data. The preliminary results, obtained on a real dataset of car users in Milan area, suggest that our method leads to beneficial effects on the...
The goal of Information Extraction is to automatically generate structured pieces of information from the relevant information contained in text documents. Machine Learning techniques have been applied to reduce the cost of Information Extraction system adaptation. However, elements of human supervision strongly bias the learning process. Unsupervised learning approaches can avoid these biases. In...
This paper deals with clustering for multi-view data, i.e. objects described by several sets of variables or proximity matrices. Many important domains or applications such as information retrieval, biology, chemistry and marketing are concerned by this problematic. The aim of this data mining research field is to search for clustering patterns that perform a consensus between the patterns from different...
Lack of supervision in clustering algorithms often leads to clusters that are not useful or interesting to human reviewers. We investigate if supervision can be automatically transferred to a clustering task in a target domain, by providing a relevant supervised partitioning of a dataset from a different source domain. The target clustering is made more meaningful for the human user by trading off...
Finding information about people using search engines is one of the most common activities on the Web. However, search engines usually return a long list of Web pages, which may be relevant to many namesakes, especially given the explosive growth of Web data. To address the challenge caused by name ambiguity in Web people search, this paper proposes a novel graph-based framework, GRAPE (abbr. a graph-based...
We present a novel method for fusing the decisions of multiple classification algorithms which use different features, classification methods, and data sources. The proposed method, called context dependent fusion of multiple algorithms (CDF-MA) is motivated by the fact that the relative performance of different algorithms can vary significantly as the characteristics of the input data vary. The training...
This paper presents a new method to acquire domain-ontology relations from semi-structured data sources. First, obtain Web documents according to the co-occurrence of concept instance and attribute value. Further, define formats of relation patterns, and extract pattern instances from Web documents, including pattern clustering and pattern combining in each cluster. Finally, relation pattern instances...
We present a system to extract definitions from a term using the Web. Definitions are organized according to a typology and its context. The structural and functional design is described; emphasizing its relevant components: the extractor of definitional context to candidates using the Yahoo!'s BOSS API; the extractor of definitional contexts and a clustering module based on textual energy as a measure...
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