30290579
Introduction to the Special Issue on Maintaining Case-Based Reasoning Systems
9824069
Data Mining Support for Case-Based Collaborative Recommendation
This paper describes ongoing research which aims to enhance collaborative recommendation techniques in the context of PTV, an applied recommender system for the TV listings domain. We have developed a case-based perspective on PTV's collaborative recommendation component, viewing the sparsity problem in collaborative filtering as one of updating and maintaining similarity knowledge for case-based systems. Our approach applies data mining techniques to extract relationships between program items that can be used to address the sparsity/ maintenance problem, as well as employing recommendation ranking that combines user similarities and item similarities to deliver more effective recommendation orderings.
1721968
A Study of Spatial Interaction Behaviour for Improved Delivery of Web-Based Maps
As access to large quantities of detailed spatial information becomes more commonplace through popular Web-based map browsing systems, the need for automatic spatial information filtering processes grows. Particulary in the case of wireless access, a suitable filtering mechanism can assist the user by personalising the map contents, giving priority to the most relevant content and eliding less relevant content. We have developed a generic solution for implicitly obtaining an insight into a user's interests and intentions based on their mouse interactions with a Web-based map browsing interface. Results of a previously reported experiment showed visual patterns in interface interaction across multiple users. This has led us to develop a new categorisation of the user interactions that can serve as a basis for more accurately weighting user interest profiles. This paper proposes a classification scheme for user interactions according to their functional properties by describing a `mouse movement hierarchy'. The value of such a weighting is demonstrated by employing the hierarchy to make a new experimental analysis of a dataset of previously logged user interactions. By properly understanding sequences of interactions, implicit inferences about the user's interests can be made, leading to improved map personalisation, ultimately improving the user's experience by delivering the most relevant content to the user in a timely fashion.
3007171
Maintaining Cased-Based Reasoners: Dimensions and Directions
Experience with the growing number of large-scale and long-term case-based reasoning (CBR) applications has led to increasing recognition of the importance of maintaining existing CBR systems. Recent research has focused on case-base maintenance (CBM), addressing such issues as maintaining consistency, preserving competence, and controlling case-base growth. A set of dimensions for case-base maintenance, proposed by Leake and Wilson, provides a framework for understanding and expanding CBM research. However, it also has been recognized that other knowledge containers can be equally important maintenance targets. Multiple researchers have addressed pieces of this more general maintenance problem, considering such issues as how to refine similarity criteria and adaptation knowledge. As with case-base maintenance, a framework of dimensions for characterizing more general maintenance activity, within and across knowledge containers, is desirable to unify and understand the state of the art, as well as to suggest new avenues of exploration by identifying points along the dimensions that have not yet been studied. This article presents such a framework by (1) refining and updating the earlier framework of dimensions for case-base maintenance, (2) applying the refined dimensions to the entire range of knowledge containers, and (3) extending the theory to include coordinated cross-container maintenance. The result is a framework for understanding the general problem of case-based reasoner maintenance (CBRM). Taking the new framework as a starting point, the article explores key issues for future CBRM research.
2995800
Improving Case-Based Recommendation: A Collaborative Filtering Approach
Data Mining, or Knowledge Discovery as it is also known, is becoming increasingly useful in a wide variety of applications. In the following paper, we lookat its use in combating some of the traditional issues faced with recommender systems. We discuss our ongoing work which aims to enhance the performance of PTV, an applied recommender system working in the TV listings domain. This system currently combines the results of separate user-based collaborative and case-based components to recommend programs to users. Our extension to this idea operates on the theory of developing a case-based view of the collaborative component itself. By using data mining techniques to extract relationships between programme items, we can address the sparsity/maintenance problem. We also adopt a unique approach to recommendation ranking which combines user similarities and item similarities to provide more effective recommendation orderings. Experimental results corroborate our ideas, demonstrating the effectiveness of data mining in improving recommender systems by providing similarity knowledge to address sparsity, both at user-based recommendation level and recommendation ranking level.
18310211
A Case Study of Case-Based CBR
Case-based reasoning depends on multiple knowledge sources beyond the case library, including knowledge about case adaptation and criteria for similarity assessment. Because hand coding this knowledge accounts for a large part of the knowledge acquisition burden for developing CBR systems, it is appealing to acquire it by learning, and CBR is a promising learning method to apply. This observation suggests developing case-based CBR systems, CBR systems whose components themselves use CBR. However, despite early interest in case-based approaches to CBR, this method has received comparatively little attention. Open questions include how case-based components of a CBR system should be designed, the amount of knowledge acquisition effort they require, and their effectiveness. This paper investigates these questions through a case study of issues addressed, methods used, and results achieved by a case-based planning system that uses CBR to guide its case adaptation and similarity assessment. The paper discusses design considerations and presents empirical results that support the usefulness of case-based CBR, that point to potential problems and tradeoffs, and that directly demonstrate the overlapping roles of different CBR knowledge sources. The paper closes with general lessons about case-based CBR and areas for future research.
204991687
Interpretation of a basic hypergeometric identity with Lie characters and Young tableaux
A combinatorial interpretation involving semistandard tableaux is provided for a four parameter terminating sum identity for the basic hypergeometric series 6?5. This identity is first produced by taking the principal specialization of a tensor product identity for two GL(n) characters.
13385001
An experimental comparison of force directed placement techniques
Force-directed placement algorithms are experimentally compared using several sample problems. Significant differences are noted in the computational efficiency of the algorithms, and in the relationship of the placement solution to the routability of the resulting board.
35531897
Decomposition methods for convolution operators
This paper presents methods for the decomposition of two-dimensional convolution operators of size m × n into sums and products of 3 × 3 operators. If m = n 2k + 1, then the maximum number of 3 × 3 operators needed in the decomposition is k2 + k − 1. In particular, every 5 × 5 operator can be decomposed into the sum and product of at most five 3 × 3 operators. A characterization is given for 5 × 5 operators which can be decomposed into the sum and product of at most three 3 × 3 operators. With operators exhibiting symmetry with respect to both axes and a diagonal, the maximum number of operators needed is k(k + 1)2.
46257718
Comparison of Traditional Brain Segmentation Tools with 3D Self-Organizing Maps
Algorithm-assisted 3D MR brain segmentation may be significantly faster than manual methods and produce visually pleasing results. We tested two- and three-dimensional region growing (2DRG and 3DRG) and self-organizing map (SOM) algorithms for segmentation of the cerebral ventricles. The SOM algorithm provides the greatest times savings, 12∶1, over manual segmentation. Concern for reproducibility of algorithm-assisted segmentation motivated an intra-operator comparative study of these and manual segmentation methods. One of us, DK, segmented the cerebral ventricles from S 3D MR-scan data sets three times manually and with the three algorithms. When variability is measured as the shape variance of derived landmarks sets, the three algorithm-assisted methods show less intra-operator variability than manual segmentation. The 2DRG and 3DRG segmentations show more variability than SOM. Of the 4 methods, SOM segmentation requires the fewest operator decisions.
206561425
Biomedical Image Analysis
The papers assembled in this special issue of Computer Vision and Image Understanding represent a selection from among those presented at the First IEEE/ SIAM Joint Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA). This meeting represented a blending of the talents and interests of individuals who attended the IEEE Workshop on Biomedical Image Analysis held in Seattle in 1994 and the Mathematical Methods in Medical Imaging Conferences previously held under SPIE sponsorship in San Diego during 1992–1994. The fulllength papers submitted to the workshop were reviewed by at least two members of the program committee and the results were compiled to produce 32 papers presented in a one-track forum on June 21–22, 1996, in San Francisco. In addition, the meeting included two plenary talks by Fred Bookstein and Demetri Terzopoulos. The primary focus of the research presented at the meeting was on the development of new methods for segmentation, registration, shape representation, and motion tracking for biomedical image analysis. The international flavor of these efforts is indicated by the fact that the individuals making the presentations represented France, Japan, Netherlands, Spain, United Kingdom, and the United States. The papers presented in this issue of Computer Vision and Image Understanding focus primarily on the task of extracting information from medical images acquired from X-ray devices, MR images, nuclear medicine scanners, and ultrasound. These are to be distinguished from images of biological structures acquired through light or confocal microscopy or the scanning electron microscope. Analysis of this type of image has expanded rapidly in scope in recent years, driven in part by advances in the parent disciplines of image formation and reconstruction as well as computer vision and shape modeling. However, the expansion owes in part to the steadily larger availability of 3D image data, which provide a playground for volumetric modeling of tissues, without dealing with projection operators encountered in optics. In order to extract information in images in a more automated, accurate, and reproducible way, methods are needed that combine the underlying image physics with explicit or implicit data structures that effectively account for data originating from living tissues. Also, since the objects under investigation in biomedical images represent the large anatomical and biological variability inherent in nature, the researcher is forced to produce methods which can process the degrees of variability in the data. The issue begins with Bookstein’s summary of developments in morphometrics (the multivariate statistics of shape) that are relevant for medical image analysis and image understanding. The second paper, by Okatani and Deguchi, offers a novel approach to the analysis of the endoscopic data increasingly arising in the course of optically guided surgery. The third and fourth papers approach the classic problem of ridge (or skeleton) extraction from medical images. They represent two distinct approaches: one more traditionally algorithmic and one set instead in the context of image evolutions.