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Seeing Stars of Valence and Arousal in Blog Posts

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2013年第1期4卷 116-123页

作者： Paltoglou, Georgios Thelwall, Michael Wolverhampton Univ Sch Technol Wolverhampton WV1 1LY W Midlands England

Sentiment analysis is a growing field of research, driven by both commercial applications and academic interest. In this paper, we explore multiclass classification of diary-like blog posts for the sentiment dimensions of valence and arousal, where the aim of the task is to predict the level of valence and arousal of a post on a ordinal five-level scale, from very negative/low to very positive/high, respectively. We show how to map discrete affective states into ordinal scales in these two dimensions, based on the psychological model of Russell's circumplex model of affect and label a previously available corpus with multidimensional, real-valued annotations. Experimental results using regression and one-versus-all approaches of support vector machine classifiers show that although the latter approach provides better exact ordinal class prediction accuracy, regression techniques tend to make smaller scale errors.

关键词： mining methods and algorithms sentiment analysis affect detection

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Text Categorization of Biomedical Data Sets Using Graph Kernels and a Controlled Vocabulary

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2013年第5期10卷 1211-1217页

作者： Bleik, Said Mishra, Meenakshi Huan, Jun Song, Min Univ Hts New Jersey Inst Technol Dept Informat Syst Newark NJ 07102 USA Univ Kansas Dept Elect Engn & Comp Sci Lawrence KS 66047 USA Yonsei Univ Dept Lib & Informat Sci Seoul 120749 South Korea

Recently, graph representations of text have been showing improved performance over conventional bag-of-words representations in text categorization applications. In this paper, we present a graph-based representation for biomedical articles and use graph kernels to classify those articles into high-level categories. In our representation, common biomedical concepts and semantic relationships are identified with the help of an existing ontology and are used to build a rich graph structure that provides a consistent feature set and preserves additional semantic information that could improve a classifier's performance. We attempt to classify the graphs using both a set-based graph kernel that is capable of dealing with the disconnected nature of the graphs and a simple linear kernel. Finally, we report the results comparing the classification performance of the kernel classifiers to common text-based classifiers.

关键词： Text categorization graph representations graph kernels biomedical ontologies mining methods and algorithms text mining classifier design and evaluation modeling structured textual and multimedia data

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Efficient Cluster Labeling for Support Vector Clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2013年第11期25卷 2494-2506页

作者： D'Orangeville, V. Mayers, M. Andre Monga, M. Ernest Wang, M. Shengrui Univ Sherbrooke Quebec City PQ Canada Univ Sherbrooke Fac Sci Mathemat Sherbrooke PQ J1K 2R1 Canada

We propose a new efficient algorithm for solving the cluster labeling problem in support vector clustering (SVC). The proposed algorithm analyzes the topology of the function describing the SVC cluster contours and explores interconnection paths between critical points separating distinct cluster contours. This process allows distinguishing disjoint clusters and associating each point to its respective one. The proposed algorithm implements a new fast method for detecting and classifying critical points while analyzing the interconnection patterns between them. Experiments indicate that the proposed algorithm significantly improves the accuracy of the SVC labeling process in the presence of clusters of complex shape, while reducing the processing time required by existing SVC labeling algorithms by orders of magnitude.

关键词： Clustering data mining mining methods and algorithms

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Discovering Temporal Change Patterns in the Presence of Taxonomies

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2013年第3期25卷 541-555页

作者： Cagliero, Luca Politecn Torino Dipartimento Automat & Informat I-10129 Turin Italy

Frequent itemset mining is a widely exploratory technique that focuses on discovering recurrent correlations among data. The steadfast evolution of markets and business environments prompts the need of data mining algorithms to discover significant correlation changes in order to reactively suit product and service provision to customer needs. Change mining, in the context of frequent itemsets, focuses on detecting and reporting significant changes in the set of mined itemsets from one time period to another. The discovery of frequent generalized itemsets, i.e., itemsets that 1) frequently occur in the source data, and 2) provide a high-level abstraction of the mined knowledge, issues new challenges in the analysis of itemsets that become rare, and thus are no longer extracted, from a certain point. This paper proposes a novel kind of dynamic pattern, namely the HIstory GENeralized Pattern (HIGEN), that represents the evolution of an itemset in consecutive time periods, by reporting the information about its frequent generalizations characterized by minimal redundancy (i.e., minimum level of abstraction) in case it becomes infrequent in a certain time period. To address HIGEN mining, it proposes HIGEN MINER, an algorithm that focuses on avoiding itemset mining followed by postprocessing by exploiting a support-driven itemset generalization approach. To focus the attention on the minimally redundant frequent generalizations and thus reduce the amount of the generated patterns, the discovery of a smart subset of HIGENs, namely the NON-REDUNDANT HIGENs, is addressed as well. Experiments performed on both real and synthetic datasets show the efficiency and the effectiveness of the proposed approach as well as its usefulness in a real application context.

关键词： Data mining mining methods and algorithms

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mining Artful Processes from Knowledge Workers' Emails

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IEEE INTERNET COMPUTING 2013年第5期17卷 10-20页

作者： Di Ciccio, Claudio Mecella, Massimo Sapienza Univ Rome Dept Comp Control & Management Sci & Engn Rome Italy

MailOfMine aims at automatically building a set of workflow models - which represent the artful processes behind knowledge workers' activities - on top of a collection of email messages. Such models formalize the unspecified agile processes that workers autonomously perform: because these models aren't defined a priori by experts but are rather inferred from real-life scenarios, they're guaranteed to respect true workflow executions. Moreover, knowledge workers can share, compare, and preserve such models to put in evidence their best practices and thus benefit the entire business.

关键词： declarative workflow models electronic mail mining methods and algorithms process mining text mining workflow management

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Capturing Social Data Evolution Using Graph Clustering

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IEEE INTERNET COMPUTING 2013年第1期17卷 74-79页

作者： Giatsoglou, Maria Vakali, Athena Aristotle Univ Thessaloniki Dept Informat Thessaloniki Greece

The fast and unpredictable evolution of social data poses challenges for capturing user activities and complex associations. Evolving social graph clustering promises to uncover the dynamics of latent user and content... 详细信息

关键词： Web mining clustering data structures database applications database management graphs and networks information search and retrieval mining methods and algorithms pattern recognition database management Database applications data clustering Data structures web mining Pattern recognition RECOVERY CATEGORY Cluster Analysis information retrieval

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mining Order-Preserving Submatrices from Data with Repeated Measurements

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2013年第7期25卷 1587-1600页

作者： Yip, Kevin Y. Kao, Ben Zhu, Xinjie Chui, Chun Kit Lee, Sau Dan Cheung, David W. Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

Order-preserving submatrices (OPSM's) have been shown useful in capturing concurrent patterns in data when the relative magnitudes of data items are more important than their exact values. For instance, in analyzing gene expression profiles obtained from microarray experiments, the relative magnitudes are important both because they represent the change of gene activities across the experiments, and because there is typically a high level of noise in data that makes the exact values untrustable. To cope with data noise, repeated experiments are often conducted to collect multiple measurements. We propose and study a more robust version of OPSM, where each data item is represented by a set of values obtained from replicated experiments. We call the new problem OPSM-RM (OPSM with repeated measurements). We define OPSM-RM based on a number of practical requirements. We discuss the computational challenges of OPSM-RM and propose a generic mining algorithm. We further propose a series of techniques to speed up two time dominating components of the algorithm. We show the effectiveness and efficiency of our methods through a series of experiments conducted on real microarray data.

关键词： Data mining bioinformatics mining methods and algorithms

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On detection of emerging anomalous traffic patterns using GPS data

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DATA & KNOWLEDGE ENGINEERING 2013年 87卷 357-373页

作者： Pang, Linsey Xiaolin Chawla, Sanjay Liu, Wei Zheng, Yu Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia Univ Melbourne Dept Comp Sci & Software Engn Melbourne Vic 3010 Australia NICTA Sydney NSW Australia

The increasing availability of large-scale trajectory data provides us great opportunity to explore them for knowledge discovery in transportation systems using advanced data mining techniques. Nowadays, large number of taxicabs in major metropolitan cities are equipped with a GPS device. Since taxis are on the road nearly 24 h a day (with drivers changing shifts), they can now act as reliable sensors to monitor the behavior of traffic. In this article, we use GPS data from taxis to monitor the emergence of unexpected behavior in the Beijing metropolitan area, which has the potential to estimate and improve traffic conditions in advance. We adapt likelihood ratio test statistic (LRT) which have previously been mostly used in epidemiological studies to describe traffic patterns. To the best of our knowledge the use of LRT in traffic domain is not only novel but results in accurate and rapid detection of anomalous behavior. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.

关键词： Data mining mining methods and algorithms Spatial/temporal databases

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A comparative analysis of classification algorithms in data mining for accuracy, speed and robustness

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INFORMATION TECHNOLOGY & MANAGEMENT 2013年第2期14卷 105-124页

作者： Dogan, Neslihan Tanrikulu, Zuhal PricewaterhouseCoopers LLP London England Bogazici Univ Dept Management Informat Syst Istanbul Turkey

Classification algorithms are the most commonly used data mining models that are widely used to extract valuable knowledge from huge amounts of data. The criteria used to evaluate the classifiers are mostly accuracy, computational complexity, robustness, scalability, integration, comprehensibility, stability, and interestingness. This study compares the classification of algorithm accuracies, speed (CPU time consumed) and robustness for various datasets and their implementation techniques. The data miner selects the model mainly with respect to classification accuracy;therefore, the performance of each classifier plays a crucial role for selection. Complexity is mostly dominated by the time required for classification. In terms of complexity, the CPU time consumed by each classifier is implied here. The study first discusses the application of certain classification models on multiple datasets in three stages: first, implementing the algorithms on original datasets;second, implementing the algorithms on the same datasets where continuous variables are discretised;and third, implementing the algorithms on the same datasets where principal component analysis is applied. The accuracies and the speed of the results are then compared. The relationship of dataset characteristics and implementation attributes between accuracy and CPU time is also examined and debated. Moreover, a regression model is introduced to show the correlating effect of dataset and implementation conditions on the classifier accuracy and CPU time. Finally, the study addresses the robustness of the classifiers, measured by repetitive experiments on both noisy and cleaned datasets.

关键词： Classification Data mining mining methods and algorithms

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SBV-Cut: Vertex-cut based graph partitioning using structural balance vertices

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DATA & KNOWLEDGE ENGINEERING 2012年 72卷 285-303页

作者： Kim, Mijung Candan, K. Selcuk Arizona State Univ Sch Comp Informat & Decis Sci Engn Tempe AZ 85281 USA

Graphs are used for modeling a large spectrum of data from the web, to social connections between individuals, to concept maps and ontologies. As the number and complexities of graph based applications increase, rendering these graphs more compact, easier to understand, and navigate through are becoming crucial tasks. One approach to graph simplification is to partition the graph into smaller parts, so that instead of the whole graph, the partitions and their inter-connections need to be considered. Common approaches to graph partitioning involve identifying sets of edges (or edge-cuts) or vertices (or vertex-cuts) whose removal partitions the graph into the target number of disconnected components. While edge-cuts result in partitions that are vertex disjoint, in vertex-cuts the data vertices can serve as bridges between the resulting data partitions;consequently, vertex-cut based approaches are especially suitable when the vertices on the vertex-cut will be replicated on all relevant partitions. A significant challenge in vertex-cut based partitioning, however, is ensuring the balance of the resulting partitions while simultaneously minimizing the number of vertices that are cut (and thus replicated). In this paper, we propose a SBV-Cut algorithm which identifies a set of balance vertices that can be used to effectively and efficiently bisect a directed graph. The graph can then be further partitioned by a recursive application of structurally-balanced cuts to obtain a hierarchical partitioning of the graph. Experiments show that SBV-Cut provides better vertex-cut based expansion and modularity scores than its competitors and works several orders more efficiently than constraint-minimization based approaches. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Clustering mining methods and algorithms Graph partitioning Vertex-cut

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