A computational model of transmedia ecosystem for story-based contents

Story-based contents (e.g., novel, movies, and computer games) have been dynamically transformed into various media. In this environment, the contents are not complete in themselves, but closely connected with each other. Also, they are not simply transformed form a medium to other media, but expanding their stories. It is called as a transmedia storytelling, and a group of contents following it is called as a transmedia ecosystem. Since the contents are highly connected in terms of the story in the transmedia ecosystem, the existing content analysis methods are hard to extract relationships between the contents. Therefore, a proper content analysis method is needed with considering expansions of the story. The aim of this work is to understand how (and why) such contents are transformed by i) defining the main features of the transmedia storytelling and ii) building the taxonomy among the transmedia patterns. More importantly, computational transmedia ecosystem is designed to process a large number of the contents, and to support high understandability of the complex transmedia patterns.     

A new Chinese character recognition approach based on the fuzzy clustering analysis

In this paper, a new Chinese character recognition (CCR) approach is proposed based on the fuzzy clustering analysis theory. Chinese characters (CCs) have various similar radicals and stroke components, which make it difficult to recognize features in the CCR process. At the same time, the recognition accuracy and the efficiency are lower when the objects to be recognized are complex. In order to solve these problems, a fuzzy clustering analysis method is introduced to enhance the computing efficiency. At first, the CCs including learning samples and testing samples are transformed into binarization templates in the form of matrixes. Then, the minimum distance algorithm is applied to calculate ‘distances’ between the testing sample templates and the learning sample templates. At last, the character recognition can be achieved by searching the minimum distance from the results. The experiment results of the CCR process can prove the effectiveness and accuracy of the new method.     

DK-means: a deterministic K-means clustering algorithm for gene expression analysis

Pattern Analysis and Applications - Clustering has been widely applied in interpreting the underlying patterns in microarray gene expression profiles, and many clustering algorithms have been...     

An algol program for dissimilarity analysis: a divisive-omnithetic clustering technique

Clustering techniques are used properly to generate hypotheses about patterns in data. Of the hierarchical techniques, those which are divisive and omnithetic possess many theoretically optimal properties. One such method, dissimilarity analysis, is implemented here in ALGOL 60, and determined to be competitive computationally with most other methods.     

MicroClAn: Microarray clustering analysis

Evaluating clustering results is a fundamental task in microarray data analysis, due to the lack of enough biological knowledge to know in advance the true partition of genes. Many quality indexes for gene clustering evaluation have been proposed. A critical issue in this domain is to compare and aggregate quality indexes to select the best clustering algorithm and the optimal parameter setting for a dataset. Furthermore, due to the huge amount of data generated by microarray experiments and the requirement of external resources such as ontologies to compute biological indexes, another critical issue is the performance decline in term of execution time. Thus, the distributed computation of algorithms and quality indexes becomes essential. Addressing these issues, this paper presents the MicroClAn framework, a distributed system to evaluate and compare clustering algorithms using the most exploited quality indexes. The best solution is selected through a two-step ranking aggregation of the ranks produced by quality indexes. A new index oriented to the biological validation of microarray clustering results is also introduced. Several scheduling strategies integrated in the framework allow to distribute tasks in the grid environment to optimize the completion time. Experimental results show the effectiveness of our aggregation strategy in identifying the best rank among different clustering algorithms. Moreover, our framework achieves good performance in terms of completion time with few computational resources.     

基于仿射传播聚类的自适应手写字符识别

对于手写字符识别过程中相似字符较多且相同字符存在大量不规则书写变形的问题,提出一种改进的仿射传播聚类算法加入手写字符识别过程中。该算法基于原始仿射传播(AP)聚类算法,将其与聚类评判函数Silhouette结合,通过AP算法迭代过程自适应地改变偏向参数以调整类别数,并且结合每次聚类质量得到最优聚类结果。基于手写汉字识别的实验结果表明,加入了原始AP算法的识别率比传统识别过程得到的识别率总体提高1.52%,而加入改进AP算法的识别率又比加入原始AP算法的识别率总体提高了1.28%。该实验结果验证了加入聚类算法于手写字符识别过程的有效性,而改进AP算法相比原始AP算法在收敛性和聚类质量上都有一定的提高。     

Formal concept analysis for topic detection: A clustering quality experimental analysis

The Topic Detection task is focused on discovering the main topics addressed by a series of documents (e.g., news reports, e-mails, tweets). Topics, defined in this way, are expected to be thematically similar, cohesive and self-contained. This task has been broadly studied from the point of view of clustering and probabilistic techniques. In this work, we propose for this task the application of Formal Concept Analysis (FCA), an exploratory technique for data analysis and organization. In particular, we propose an extension of FCA-based methods for topic detection applied in the literature by applying the stability concept for the topic selection. The hypothesis is that FCA will enable the better organization of the data and stability the better selection of topics based on this data organization, thus better fulfilling the task requirements by improving the quality and accuracy of the topic detection process. In addition, the proposed FCA-based methodology is able to cope with some well-known drawbacks that clustering and probabilistic methodologies present, such as: the need to set a predefined number of clusters or the difficulty in dealing with topics with complex generalization-specialization relationships. In order to prove this hypothesis, the FCA operation is compared to other established techniques — Hierarchical Agglomerative Clustering (HAC) and Latent Dirichlet Allocation (LDA). To allow this comparison, these approaches have been implemented by the authors in a novel experimental framework. The quality of the topics detected by the different approaches in terms of their suitability for the topic detection task is evaluated by means of internal clustering validity metrics. This evaluation demonstrates that FCA generates cohesive clusters, which are less subject to changes in cluster granularity. Driven by the quality of the detected topics, FCA achieves the best general outcome, improving the experimental results for Topic Detection Task at the 2013 Replab Campaign.     

智能优化算法在聚类分析中的应用

聚类分析是数据挖掘的重要技术,可根据数据间的相似程度,将数据进行分类,现已广泛应用于工程和技术等领域中。元胞蚁群算法是在将元胞自动机的邻居和规则引入传统蚁群算法的基础上,利用元胞在离散元胞空间的演化规律和蚁群寻优特点的新型优化算法。针对聚类分析的特点,利用元胞蚁群算法进行求解,经实验测试和验证,获得了较好的结果。     

一种新的混合聚类分析算法*

结合人工鱼群算法的全局寻优优点提出了一种基于人工鱼群算法的K-平均混合聚类分析算法。实验结果表明,该算法能克服K-平均聚类算法易陷入局部极小的不足,有较好的全局性,且聚类正确率明显高于K-平均算法,聚类效果更好。     

Spatial clustering procedures for region analysis

This paper describes two clustering procedures for region analysis of image data and discusses the security of these algorithms theoretically. First our algorithms find kernels of regions and then classify pixels into regions using these kernels. The first algorithm distinguishes the regions that have far more distances than the given distance and the second algorithm distinguishes C regions that are great distances from each other in the feature space. These parameters are criteria which decide whether regions are similar or dissimilar. Examples are presented in order to show how these algorithms work for real image data.     

HandPuppet3D: Motion capture and analysis for character animation

Motion capture is a technique of digitally recording the movements of real entities, usually humans. It was originally developed as an analysis tool in biomechanics research, but has grown increasingly important as a source of motion data for computer animation. In this context it has been widely used for both cinema and video games. Hand motion capture and tracking in particular has received a lot of attention because of its critical role in the design of new Human Computer Interaction methods and gesture analysis. One of the main difficulties is the capture of human hand motion. This paper gives an overview of ongoing research “HandPuppet3D” being carried out in collaboration with an animation studio to employ computer vision techniques to develop a prototype desktop system and associated animation process that will allow an animator to control 3D character animation through the use of hand gestures. The eventual goal of the project is to support existing practice by providing a softer, more intuitive, user interface for the animator that improves the productivity of the animation workflow and the quality of the resulting animations. To help achieve this goal the focus has been placed on developing a prototype camera based desktop gesture capture system to capture hand gestures and interpret them in order to generate and control the animation of 3D character models. This will allow an animator to control 3D character animation through the capture and interpretation of hand gestures. Methods will be discussed for motion tracking and capture in 3D animation and in particular that of hand motion tracking and capture. HandPuppet3D aims to enable gesture capture with interpretation of the captured gestures and control of the target 3D animation software. This involves development and testing of a motion analysis system built from algorithms recently developed. We review current software and research methods available in this area and describe our current work.     

Application of feature selection methods for automated clustering analysis: a review on synthetic datasets

The effective modelling of high-dimensional data with hundreds to thousands of features remains a challenging task in the field of machine learning. This process is a manually intensive task and requires skilled data scientists to apply exploratory data analysis techniques and statistical methods in pre-processing datasets for meaningful analysis with machine learning methods. However, the massive growth of data has brought about the need for fully automated data analysis methods. One of the key challenges is the accurate selection of a set of relevant features, which can be buried in high-dimensional data along with irrelevant noisy features, by choosing a subset of the complete set of input features that predicts the output with higher accuracy comparable to the performance of the complete input set. Kohonen’s self-organising neural network map has been utilised in various ways for this task, such as with the weighted self-organising map (WSOM) approach and this method is reviewed for its efficacy. The study demonstrates that the WSOM approach can result in different results on different runs on a given dataset due to the inappropriate use of the steepest descent optimisation method to minimise the weighted SOM’s cost function. An alternative feature weighting approach based on analysis of the SOM after training is presented; the proposed approach allows the SOM to converge before analysing the input relevance, unlike the WSOM that aims to apply weighting to the inputs during the training which distorts the SOM’s cost function, resulting in multiple local minimums meaning the SOM does not consistently converge to the same state. We demonstrate the superiority of the proposed method over the WSOM and a standard SOM in feature selection with improved clustering analysis.

     

SignatureClust: a tool for landmark gene-guided clustering

Over the last several years, many clustering algorithms have been applied to gene expression data. However, most clustering algorithms force the user into having one set of clusters, resulting in a restrictive biological interpretation of gene function. It would be difficult to interpret the complex biological regulatory mechanisms and genetic interactions from this restrictive interpretation of microarray expression data. The software package SignatureClust allows users to select a group of functionally related genes (called ‘Landmark Genes’), and to project the gene expression data onto these genes. Compared to existing algorithms and software in this domain, our software package offers two unique benefits. First, by selecting different sets of landmark genes, it enables the user to cluster the microarray data from multiple biological perspectives. This encourages data exploration and discovery of new gene associations. Second, most packages associated with clustering provide internal validation measures, whereas our package validates the biological significance of the new clusters by retrieving significant ontology and pathway terms associated with the new clusters. SignatureClust is a free software tool that enables biologists to get multiple views of the microarray data. It highlights new gene associations that were not found using a traditional clustering algorithm. The software package ‘SignatureClust’ and the user manual can be downloaded from .     

Weighted-object ensemble clustering: methods and analysis

In recent years, research on provenance has increased exponentially, and such studies in the field of business process monitoring have been especially remarkable. Business process monitoring deals with recording information about the actual execution of processes to then extract valuable knowledge that can be utilized for business process quality improvement. In prior research, we developed an occurrence-centric approach built on our notion of occurrence that provides a holistic perspective of system dynamics. Based on this concept, more complex structures are defined herein, namely Occurrence Base (OcBase) and Occurrence Management System (OcSystem), which serve as scaffolding to develop business process monitoring systems. This paper focuses primarily on the critical provenance task of extracting valuable knowledge from such systems by proposing an Occurrence Query Framework that includes the definition of an Occurrence Base Metamodel and an Occurrence Query Language based on this metamodel. Our framework provides a way of working for the construction of business process monitoring systems that are provenance aware. As a proof of concept, a tool implementing the various components of the framework is presented. This tool has been tested against a real system in the context of biobanks.     

Sequential Combination Methods for Data Clustering Analysis

This paper proposes the use of more than one clustering method to improve clustering performance,Clustering is an optimization procedure based on a specific clustering criterion.Clustering combination can be regarded as a technique that constructs and processes multiple clustering criteria.Since the global and local clustering criteria are complementary rather than competitive,combining these two types of clustering criteria may enhance the clustering performance,In our past work,a multi-objective programming based simultaneous clustering combination algorithm has been propsed,which incorporates multiple criteria into an objective function by a weighting method,and solves this problem with constrained nonlinear optimization programming.But this algorithm has high computaional complexity,Here a sequential combination approach is investigated,which first uses the global criterion based clustering to produce an initial result ,then uses the local criterion based informaiton to improve the initial result with a probabilistic relaxation algorithm or linear additive model.Compared with the simultaneous combination method,sequential combination has low computational complexity.Results on some simulated data and standard test data are reported.It appears that clustering performance improvement can be achieved at low cost through sequential combination.     

移动数据预估聚类分析算法

研究了一种移动数据的预估聚类分析算法。首先建立移动数据的数学模型,然后在此模型的基础上,提出一个基于微簇的移动数据的聚类分析算法,并对移动微簇的相交和分裂事件进行了详细地分析。提出的新算法可以预测一定时间段内的任意时刻数据的聚类情况。     

利用总体特征对中文签字进行分类的聚类算法

针对不同书写者书写同一字的分类问题,在C 均值法和马氏距离测度的基础之上,提出了一种动态聚类算法,并讨论了签字的总体特征选择问题。利用该聚类算法对不同书写者的签字进行二分分类得到了较好的效果。实验显示,选择一组代表书写者书写风格的特征是分类成败的关键。文中选取的五个总体特征应用到非模仿的签字鉴别中有较好效果。     

AssetCollector: a system for managing collections of cultural assets

In this paper, AssetCollector is presented, which is a system for managing collections of cultural assets. AssetCollector covers the needs of collection curators towards defining, populating and searching a collection in a flexible way, while supporting them in generating reports based on the collection’s assets and reusing them in order to build web sites and CD-ROMs. In order to support the above functionality, the system provides the content structuring subsystem, the content input subsystem, the search subsystem and the report subsystem. The use of the subsystems is straightforward and requires no technical skills from the curators. AssetCollector has been successfully applied for organizing various collections of cultural assets in Greece, such as archaeological sites, museums and published books. In the future, an evaluation procedure is planned in order to further refine the use of the system according to the targeted users’ needs. Furthermore, more import and export facilities will be provided, which will make the system compliant with widely accepted standards.     

Clock synchronization for Internet measurements: a clustering algorithm

Clock synchronization is a crucial issue for scalable and accurate network performance measurements, especially when no external time sources are introduced. The paper presents a clustering based efficient and robust algorithm Optimized Top-Down Time series Segmentation (OTDTS) for clock synchronization between end-to-end systems. The computational complexity of OTDTS is of order O(KN²). Based on the one-way probe delay traces, the algorithm segments the delay time series at proper points, at which clock dynamics occur. End systems could achieve relative clock synchronization by estimating and removing the clock skew of each segment. Simulations on artificial data set and practical Internet measurement illustrate the availability and efficiency of OTDTS.