3rd Workshop in Symbolic Data Analysis: book of abstracts

This workshop is the third regular meeting of researchers interested in Symbolic Data Analysis. The main aim of the event is to favor the meeting of people and the exchange of ideas from different fields - Mathematics, Statistics, Computer Science, Engineering, Economics, among others - that contribute to Symbolic Data Analysis

Euclidean Distances, soft and spectral Clustering on Weighted Graphs

We define a class of Euclidean distances on weighted graphs, enabling to perform thermodynamic soft graph clustering. The class can be constructed form the "raw coordinates" encountered in spectral clustering, and can be extended by means of higher-dimensional embeddings (Schoenberg transformations). Geographical flow data, properly conditioned, illustrate the procedure as well as visualization aspects.Comment: accepted for presentation (and further publication) at the ECML PKDD 2010 conferenc

Sparse Randomized Shortest Paths Routing with Tsallis Divergence Regularization

This work elaborates on the important problem of (1) designing optimal randomized routing policies for reaching a target node t from a source note s on a weighted directed graph G and (2) defining distance measures between nodes interpolating between the least cost (based on optimal movements) and the commute-cost (based on a random walk on G), depending on a temperature parameter T. To this end, the randomized shortest path formalism (RSP, [2,99,124]) is rephrased in terms of Tsallis divergence regularization, instead of Kullback-Leibler divergence. The main consequence of this change is that the resulting routing policy (local transition probabilities) becomes sparser when T decreases, therefore inducing a sparse random walk on G converging to the least-cost directed acyclic graph when T tends to 0. Experimental comparisons on node clustering and semi-supervised classification tasks show that the derived dissimilarity measures based on expected routing costs provide state-of-the-art results. The sparse RSP is therefore a promising model of movements on a graph, balancing sparse exploitation and exploration in an optimal way

Unsupervised and semi-supervised clustering with learnable cluster dependent kernels.

Despite the large number of existing clustering methods, clustering remains a challenging task especially when the structure of the data does not correspond to easily separable categories, and when clusters vary in size, density and shape. Existing kernel based approaches allow to adapt a specific similarity measure in order to make the problem easier. Although good results were obtained using the Gaussian kernel function, its performance depends on the selection of the scaling parameter. Moreover, since one global parameter is used for the entire data set, it may not be possible to find one optimal scaling parameter when there are large variations between the distributions of the different clusters in the feature space. One way to learn optimal scaling parameters is through an exhaustive search of one optimal scaling parameter for each cluster. However, this approach is not practical since it is computationally expensive especially when the data includes a large number of clusters and when the dynamic range of possible values of the scaling parameters is large. Moreover, it is not trivial to evaluate the resulting partition in order to select the optimal parameters. To overcome this limitation, we introduce two new fuzzy relational clustering techniques that learn cluster dependent Gaussian kernels. The first algorithm called clustering and Local Scale Learning algorithm (LSL) minimizes one objective function for both the optimal partition and for cluster dependent scaling parameters that reflect the intra-cluster characteristics of the data. The second algorithm, called Fuzzy clustering with Learnable Cluster dependent Kernels (FLeCK) learns the scaling parameters by optimizing both the intra-cluster and the inter-cluster dissimilarities. Consequently, the learned scale parameters reflect the relative density, size, and position of each cluster with respect to the other clusters. We also introduce semi-supervised versions of LSL and FLeCK. These algorithms generate a fuzzy partition of the data and learn the optimal kernel resolution of each cluster simultaneously. We show that the incorporation of a small set of constraints can guide the clustering process to better learn the scaling parameters and the fuzzy memberships in order to obtain a better partition of the data. In particular, we show that the partial supervision is even more useful on real high dimensional data sets where the algorithms are more susceptible to local minima. All of the proposed algorithms are optimized iteratively by dynamically updating the partition and the scaling parameter in each iteration. This makes these algorithms simple and fast. Moreover, our algorithms are formulated to work on relational data. This makes them applicable to data where objects cannot be represented by vectors or when clusters of similar objects cannot be represented efficiently by a single prototype. Our extensive experiments show that FLeCK and SS-FLeCK outperform existing algorithms. In particular, we show that when data include clusters with various inter-cluster and intra-cluster distances, learning cluster dependent kernel is crucial in obtaining a good partition

Semantic Similarity of Spatial Scenes

The formalization of similarity in spatial information systems can unleash their functionality and contribute technology not only useful, but also desirable by broad groups of users. As a paradigm for information retrieval, similarity supersedes tedious querying techniques and unveils novel ways for user-system interaction by naturally supporting modalities such as speech and sketching. As a tool within the scope of a broader objective, it can facilitate such diverse tasks as data integration, landmark determination, and prediction making. This potential motivated the development of several similarity models within the geospatial and computer science communities. Despite the merit of these studies, their cognitive plausibility can be limited due to neglect of well-established psychological principles about properties and behaviors of similarity. Moreover, such approaches are typically guided by experience, intuition, and observation, thereby often relying on more narrow perspectives or restrictive assumptions that produce inflexible and incompatible measures. This thesis consolidates such fragmentary efforts and integrates them along with novel formalisms into a scalable, comprehensive, and cognitively-sensitive framework for similarity queries in spatial information systems. Three conceptually different similarity queries at the levels of attributes, objects, and scenes are distinguished. An analysis of the relationship between similarity and change provides a unifying basis for the approach and a theoretical foundation for measures satisfying important similarity properties such as asymmetry and context dependence. The classification of attributes into categories with common structural and cognitive characteristics drives the implementation of a small core of generic functions, able to perform any type of attribute value assessment. Appropriate techniques combine such atomic assessments to compute similarities at the object level and to handle more complex inquiries with multiple constraints. These techniques, along with a solid graph-theoretical methodology adapted to the particularities of the geospatial domain, provide the foundation for reasoning about scene similarity queries. Provisions are made so that all methods comply with major psychological findings about people’s perceptions of similarity. An experimental evaluation supplies the main result of this thesis, which separates psychological findings with a major impact on the results from those that can be safely incorporated into the framework through computationally simpler alternatives

A Comparative Study of Dimensionality Reduction Techniques to Enhance Trace Clustering Performances

Technology Management/ Information System/ EntrepreneurshipProcess mining aims at extracting useful information from event logs. Recently, in order to improve processes, several organizations such as high-tech companies, hospitals, and municipalities utilize process mining techniques. Real-life process logs from such organizations are usually very large and complicated, since the process logs in general contain numerous activities which are executed by many employees. Furthermore, lots of real-life process logs generate spaghetti-like process models due to the complexity of processes. Traditional process mining techniques have problems with discovering and analyzing real-life process logs which come from less structured processes. To overcome the weaknesses of traditional process mining techniques, a trace clustering has been developed. The trace clustering splits an event log into several subsets, and each subset contains homogenous cases. Even though the trace clustering is useful to handle complex process logs, it is time-consuming and computationally expensive due to a large number of features generated from complex logs. In this thesis, we applied dimensionality reduction (preprocessing) techniques to the trace clustering in order to reduce the number of features. To validate our approach, we conducted experiments to discover relationships between dimensionality reduction techniques and clustering algorithms, and we performed a case study which involves patient treatment processes of a hospital. Among many dimensionality reduction techniques, we used three techniques namely singular value decomposition (SVD), random projection, and principal components analysis (PCA). The result shows that the trace clustering with dimensionality reduction techniques produce higher average fitness values. Furthermore, processing time of trace clustering is effectively reduced with dimensionality reduction techniques. Moreover, we measured similarity between clustering results to observe the degree of changes in clustering results while applying dimensionality reduction techniques. The similarity is resulted differently according to used clustering algorithm.ope

Web-based strategies in the manufacturing industry

The explosive growth of Internet-based architectures is allowing an efficient access to information resources over geographically dispersed areas. This fact is exerting a major influence on current manufacturing practices. Business activities involving customers, partners, employees and suppliers are being rapidly and efficiently integrated through networked information management environments. Therefore, efforts are required to take advantage of distributed infrastructures that can satisfy information integration and collaborative work strategies in corporate environments. In this research, Internet-based distributed solutions focused on the manufacturing industry are proposed. Three different systems have been developed for the tooling sector, specifically for the company Seco Tools UK Ltd (industrial collaborator). They are summarised as follows. SELTOOL is a Web-based open tool selection system involving the analysis of technical criteria to establish appropriate selection of inserts, toolholders and cutting data for turning, threading and grooving operations. It has been oriented to world-wide Seco customers. SELTOOL provides an interactive and crossed-way of searching for tooling parameters, rather than conventional representation schemes provided by catalogues. Mechanisms were developed to filter, convert and migrate data from different formats to the database (SQL-based) used by SELTOOL.TTS (Tool Trials System) is a Web-based system developed by the author and two other researchers to support Seco sales engineers and technical staff, who would perform tooling trials in geographically dispersed machining centres and benefit from sharing data and results generated by these tests. Through TTS tooling engineers (authorised users) can submit and retrieve highly specific technical tooling data for both milling and turning operations. Moreover, it is possible for tooling engineers to avoid the execution of new tool trials knowing the results of trials carried out in physically distant places, when another engineer had previously executed these trials. The system incorporates encrypted security features suitable for restricted use on the World Wide Web. An urgent need exists for tools to make sense of raw data, extracting useful knowledge from increasingly large collections of data now being constructed and made available from networked information environments. This explosive growth in the availability of information is overwhelming the capabilities of traditional information management systems, to provide efficient ways of detecting anomalies and significant patterns in large sets of data. Inexorably, the tooling industry is generating valuable experimental data. It is a potential and unexplored sector regarding the application of knowledge capturing systems. Hence, to address this issue, a knowledge discovery system called DISKOVER was developed. DISKOVER is an integrated Java-application consisting of five data mining modules, able to be operated through the Internet. Kluster and Q-Fast are two of these modules, entirely developed by the author. Fuzzy-K has been developed by the author in collaboration with another research student in the group at Durham. The final two modules (R-Set and MQG) have been developed by another member of the Durham group. To develop Kluster, a complete clustering methodology was proposed. Kluster is a clustering application able to combine the analysis of quantitative as well as categorical data (conceptual clustering) to establish data classification processes. This module incorporates two original contributions. Specifically, consistent indicators to measure the quality of the final classification and application of optimisation methods to the final groups obtained. Kluster provides the possibility, to users, of introducing case-studies to generate cutting parameters for particular Input requirements. Fuzzy-K is an application having the advantages of hierarchical clustering, while applying fuzzy membership functions to support the generation of similarity measures. The implementation of fuzzy membership functions helped to optimise the grouping of categorical data containing missing or imprecise values. As the tooling database is accessed through the Internet, which is a relatively slow access platform, it was decided to rely on faster Information retrieval mechanisms. Q-fast is an SQL-based exploratory data analysis (EDA) application, Implemented for this purpose

Knowledge Graph Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness

Presently, knowledge graph-based recommendation algorithms have garnered considerable attention among researchers. However, these algorithms solely consider knowledge graphs with single relationships and do not effectively model exercise-rich features, such as exercise representativeness and informativeness. Consequently, this paper proposes a framework, namely the Knowledge-Graph-Exercise Representativeness and Informativeness Framework, to address these two issues. The framework consists of four intricate components and a novel cognitive diagnosis model called the Neural Attentive cognitive diagnosis model. These components encompass the informativeness component, exercise representation component, knowledge importance component, and exercise representativeness component. The informativeness component evaluates the informational value of each question and identifies the candidate question set that exhibits the highest exercise informativeness. Furthermore, the skill embeddings are employed as input for the knowledge importance component. This component transforms a one-dimensional knowledge graph into a multi-dimensional one through four class relations and calculates skill importance weights based on novelty and popularity. Subsequently, the exercise representativeness component incorporates exercise weight knowledge coverage to select questions from the candidate question set for the tested question set. Lastly, the cognitive diagnosis model leverages exercise representation and skill importance weights to predict student performance on the test set and estimate their knowledge state. To evaluate the effectiveness of our selection strategy, extensive experiments were conducted on two publicly available educational datasets. The experimental results demonstrate that our framework can recommend appropriate exercises to students, leading to improved student performance.Comment: 31 pages, 6 figure

Clustering-Based Pre-Processing Approaches To Improve Similarity Join Techniques

Research on similarity join techniques is becoming one of the growing practical areas for study, especially with the increasing E-availability of vast amounts of digital data from more and more source systems. This research is focused on pre-processing clustering-based techniques to improve existing similarity join approaches. Identifying and extracting the same real-world entities from different data sources is still a big challenge and a significant task in the digital information era. Dissimilar extracts may indeed represent the same real-world entity because of inconsistent values and naming conventions, incorrect or missing data values, or incomplete information. Therefore discovering efficient and accurate approaches to determine the similarity of data objects or values is of theoretical as well as practical significance. Semantic problems are raised even on the concept of similarity regarding its usage and foundation. Existing similarity join approaches often have a very specific view of similarity measures and pre-defined predicates that represent a narrow focus on the context of similarity for a given scenario. The predicates have been assumed to be a group of clustering [MSW 72] related attributes on the join. To identify those entities for data integration purposes requires a broader view of similarity; for instance a number of generic similarity measures are useful in a given data integration systems. This study focused on string similarity join, namely based on the Levenshtein or edit distance and Q-gram. Proposed effective and efficient pre-processing clustering-based techniques were the focus of this study to identify clustering related predicates based on either attribute value or data value that improve existing similarity join techniques in enterprise data integration scenarios