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R. Bellazzi,
L. Ironi,
R. Guglielmann,
and M. Stefanelli.
Qualitative models and fuzzy systems: an integrated approach for learning from data.
AIM,
14:5--28,
1998.
Keywords:
Fuzzy Models.
| Abstract: |
This paper presents a method for the identification of the dynamics of non-linear systems by learning from data. The key idea which underlies our approach consists of the integration of qualitative modelling techniques with fuzzy logic systems. The resulting hybrid method exploits the a priori structural knowledge on the system to initialize a fuzzy inference procedure which determines, from the available experimental data, a functional approximation of the system dynamics that can be used as a reasonable predictor of the patient's future state. The major advantage which results from such an integrated framework lies in a significant improvement of both efficiency and robustness of identification methods based on fuzzy models which learn an input-output relation from data. As a benchmark of our method, we have considered the problem of identifying the response to the insulin therapy from insulin-dependent diabetic patients: the results obtained are presented and discussed in the paper. |
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Claudio Bettini,
X. Sean Wang,
Sushil Jajodia,
and Jia-Ling Lin.
Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences.
TKDE,
10(2):222-237,
1998.
Keywords:
Sequential/Temporal Data,
Sequential/Temporal Patterns.
| Abstract: |
An important usage of time sequences is to discover temporal patterns. The discovery process usually starts with a user-specified skeleton, called an {\sl event structure} which consists of a number of variables representing events and temporal constraints among these variables; the goal of the data mining is to find temporal patterns, i.e., instantiations of the variables in the structure, that appear frequently in the time sequence. This paper introduces event structures that have temporal constraints with multiple granularities, defines the pattern-discovery problem with these structures, and studies effective algorithms to solve it. The basic components of the algorithms include timed automata with granularities (TAGs) and a number of heuristics. The TAGs are for testing whether a specific temporal pattern, called a candidate complex event type, appears frequently in a time sequence. Since there are often a huge number of candidate event types for a usual event structure, heuristics are presented aiming at reducing the number of candidate event types and reducing the time spent by the TAGs testing whether a candidate type does appear frequently in the sequence. These heuristics exploit the information provided by explicit and implicit temporal constraints with granularity in the given event structure. The paper also gives the results of an experiment to show the effectiveness of the heuristics on a real data set. |
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James C. Bezdek and Nikhil R. Pal.
Some New Indexes of Cluster Validity.
SMCB,
28(3):301--315,
1998.
Keywords:
Noise Handling,
Clustering,
Cluster Validity Measures.
| Abstract: |
We review two clustering algorithms (hard c-means and single linkage) and three indexes of crisp cluster validity (Hubert's statistics, the Davies-Bouldin index, and Dunn's index). We illustrate two deficiencies of Dunn's index which make it overly sensitive to noisy clusters and propose several generalizations of it that are not as brittle to outliers in the clusters. Our numerical examples show that the standard measure of interset distance (the minimum distance between points in a set) is the {\sl worst} (least reliable) measure upon which to base cluster validation indexes when the clusters are expected to form volumetric clouds. Experimental results also suggest that intercluster separation plays a more important role in cluster validation than cluster diameter. Our simulations show that while Dunn's original index has operational flaws, the concept it embodies provides a rich paradigm for validation of partitions that have cloud-like clusters. Five of our generalized Dunn's indexes provide the best validation results for the simulations presented. |
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Sergey Brin and Lawrence Page.
The anatomy of a large-scale hypertextual Web search engine.
Computer Networks and ISDN Systems,
30:107--117,
1998.
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Antonio C. Capelo,
Liliana Ironi,
and Stefania Tentoni.
Automated Mathematical Modelling from Experimental Data: An Application to Material Science.
SMCC,
28(3):356--370,
1998.
| Abstract: |
Automated model formulation is a crucial issue toward the construction of computational environments that can reason about the behaviour of a physical system. The procedure of mathematically modelling a given physical system is quite complex and basically involves three fundamental entities: the experimental data, a set of candidate models, and rules for determining in such a set the ``best'' model that reproduces the measured data. The construction of the candidate model is domain dependent and based on specific knowledge and techniques of the application domain. The choice of the best model is guided by the data themselves; a first rough guess, which is suggested by the qualitative properties of the observed behaviour, is refined through system identification techniques so that the quantitative properties of the observed behaviour are assessed. Therefore, automating such a procedure requires handling and integrating different formalisms and methods, both qualitative and quantitative. This paper describes a comprehensive environment that aims at the automated formulation of an accurate quantitative model of the mechanical behaviour of an actual viscoelastic material in accordance with the observed response of the material to standard experiments. To this end, algorithms and methods for both the generation of an exhaustive library of models of ideal materials and the selection of the most ``accurate'' model of a real material have been designed and implemented. The model selection phase occurs in two main stages; at first, the subset of most plausible candidate models for the material is drawn out from the library in accordance with the qualitative properties of the material that are highlighted by the experimental data; then, the most accurate model of the material is identified within such a set by exploiting both statistical and numerical methods. |
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Wei-Ge Chen,
Georgios B. Giannakis,
and N. Nandhakumar.
A Harmonic Retrieval Framework for Discontinuous Motion Estimation.
TIP,
7(9):1242--1257,
1998.
Keywords:
Image Data.
| Abstract: |
Motion discontinuities arise when there are occlusions or multiple moving objects in the scene that is imaged. Conventional regularization techniques use smoothness constraints but are not applicable to motion discontinuities. In this paper, we show that discontinuous (or multiple) motion estimation can be viewed as a multicomponent harmonic retrieval problem. From this viewpoint, a number of established techniques for harmonic retrieval can be applied to solve the challenging problem of discontinuous (or multiple) motion. Compared with existing techniques, the resulting algorithm is not iterative, which not only implies computational efficiency but also obviates concerns regarding convergence or local minima. It also adds flexibility to spatio-temporal techniques which have suffered from lack of explicit modelling discontinuous motion. Experimental verification of our framework on both synthetic as well as real image data is provided. |
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Jian-Qin Chen,
Yu-Geng Xi,
and Zhong-Jun Zhang.
A clustering algorithm for fuzzy model identification.
FSS,
98:319--329,
1998.
Keywords:
Clustering,
Fuzzy Models,
Sequential/Temporal Data.
| Abstract: |
The fuzzy model proposed by Takagi and Sugeno can represent highly nonlinear systems and is widely used for the representation of fuzzy rules. In this paper, the model is firstly modified to make its identification easier. Base on the fuzzy $c$-partition space, four criteria are proposed for optimization of the model parameters. Following that, a clustering algorithm composed of fuzzy $c$-linear functions clustering and like fuzzy $c$-means clustering is developed for minimizing the four criteria. An identification scheme for rule's premise and consequence parameters is deduced from the clustering algorithm in succession. Finally, four examples are demonstrated to verify the effectiveness of the proposed algorithm. |
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Yasser El-Sonbaty and M. A. Ismail.
Fuzzy Clustering for Symbolic Data.
TFS,
6(2):195--204,
1998.
Keywords:
Clustering,
Fuzzy Clustering.
| Abstract: |
Most of the techniques used in the literature in clustering symbolic data are based on the hierarchical methodology, which utilizes the concept of agglomerative or divisive methods as the core of the algorithm. The main contribution of this paper is to show how to apply the concept of fuzziness on a data set of symbolic objects and how to use this concept in formulating the clustering problem of symbolic objects as a partitioning problem. Finally, a fuzzy symbolic c-means algorithm is introduced as an application of applying and testing the proposed algorithm on real and synthetic data sets. The results of the application of the new algorithm show that the new technique is quite efficient and, in many respects, superior to traditional methods of hierarchical nature. |
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Amir B. Geva.
Feature Extraction and State Identification in Biomedical Signals using Hierarchical Fuzzy Clustering.
MBEC,
36:608--614,
1998.
Keywords:
Clustering,
Cluster Validity Measures,
Fuzzy Clustering,
Sequential/Temporal Patterns,
Medical Applications.
| Abstract: |
Many problems in the field of biomedical signal processing can be reduced to a task of state recognition and event prediction. Examples can be found in tachycardia detection from ECG signals, epileptic seizure or psychotic attack prediction from an EEG signal, and prediction of vehicle drivers falling asleep from both signals. The problem generally treats a set of ordered measurements and asks for the recognition of some patterns of observed elemtns that will forecast an event or a transition between two different states of the biological system. It is proposed to apply clustering methods to grouping discontinuous related temporal patterns of a continuously sampled measurement. The vague switches from one stationary state to another are naturally trated by means of fuzzy clustering. In such cases, an adaptive selection of the number of clusters (the number of underlying semi-stationary processes) can overcome the general non-stationary nature of biomedical signals and enable the formation of a warning cluster. The algorithm suggested for the clustering is a new recursive algorithm for hierarchical fuzzy partitioning. Each pattern can have a non-zero membership on more than one data subset in the hierarchy. A `natural' and feasible solution to the cluster validity problem is suggested by combining hierarchical and fuzzy concepts. The algorithm is shown to be effective for a variety of data sets with a wide dynamic range of both covariance matrices and number of members in each class. The new method is applied to state recognition during recovery from exercise using the heart rate signal and to the forecasting of generalised epileptic seizures from the EEG signal. |
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Amir B. Geva.
ScaleNet -- Multiscale Neural-Network Architecture for Time Series Prediction.
TNN,
9(5):1471--1482,
1998.
Keywords:
Clustering,
Neural Networks,
Wavelets,
Multiscale Analysis,
Sequential/Temporal Data,
Sequential/Temporal Patterns.
| Abstract: |
The effectiveness of a multiscale neural-network (NN) architecture for the time series prediction of nonlinear dynamic systems has been investigated. The prediction task is simplified by decomposing different scales of past windows into different scales of wavelets (local frequencies), and predicting the coefficients of each scale of wavelets by means of a separate multilayer percepetron NN. The short-term history (short past windows) is decomposed into the lower scales of wavelet coefficients (higher frequencies) which are utilitzed for ``detailed'' analysis and prediction, while the long-term history (long past window) is decomposed into higher scales of wavelet coefficients (low frequencies) that are used for the analysis and prediction of slow trends in the time series. These coordinated scales of time and frequency provides an interpretation of the series structures, and more information about the history of the series, using fewer coefficients than other methods. The prediction's results concerning all the different scales of time and frequencies are combined by another ``expert'' perceptron NN which learns the weight of each scale in the goal-prediction of the original time series. Each network is trained by the backpropagation algorithm using the Levenberg-Marquardt method. The weights and biases are initialized by a new clustering algorithm of the temporal patterns of the time series, which improves the prediction results as compared to random initialization. Three main sets of data were analyzed: the sunspots' benchmark, fluctuations in a far-infrared laser and a nonlinear numerically generated series. Taking the ultimate goal to be the accuracy of the prediction, we found that the suggested multiscale architecture outperforms the corresponding single-scale architectures. The employment of improved learning methods for each of the ScaleNet networks can further improve the prediction results. |
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Pedro Julian,
Mario Jordán,
and Alfredo Desages.
Canonical Piecewise-Linear Approximation of Smooth Functions.
TCS1,
45(5):567--571,
1998.
Keywords:
Piecewise Linear Representations.
| Abstract: |
This paper deals with the approximation of smooth functions using canonical piecewise-linear functions. The developing of tools in the field of analysis and control of nonlinear systems based on this kind of functions, as well as its efficiency in the representation of electronic devices, motivated the development of useful methods to obtain accurate approximations. A recursive method is proposed to obtain simultaneously all the parameters required and its convergence is studied. In addition, an iterative method to introduce new partitions on the domain, when the error obtained is not satisfactory, is described. This method takes advantage of the partitions already found to reduce the total number of parameters that the algorithm has to handle. |
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M. Kundu,
M. Nasipuri,
and D. K. Basu.
A Knowledge-Based Approach to ECG Interpretation Using Fuzzy Logic.
SMCB,
28(2):237--243,
1998.
Keywords:
Classification,
Medical Applications.
| Abstract: |
A rule-based expert system which uses generalized modus ponens (GMP) from fuzzy logic as a rule of inference is described here for classification of abnormalities related to rhythm disorder in the human heart, through interpretation of the patient's electrocardiographic (ECG) patterns. Application of GMP makes diagnosis of a wide range of variations in the input ECG patterns possible even if they differ from the patterns defined in the preconditions of the rules of the rulebase. The work shows how fuzzy logic with suitably drawn possibility distributions of variables of cardiological domain plays a significant role in making the expert system sensitive to finer variations of input ECG patterns, which are very common in bioelectric signals, without enhancing the size of the rulebase. |
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Sven Loncaric.
A Survey of Shape Analysis Techniques.
PR,
31(8):983--1001,
1998.
Keywords:
Classification,
Surveys.
| Abstract: |
This paper provides a review of shape analysis methods. Shape analysis methods play an important role in systems for object recognition, matching, registration, and analysis. Research in shape analysis has been motivated, in part, by studies of human visual form perception systems. Several theories of visual form perception are briefly mentioned. Shape analysis methods are classified into several groups. Classification is determined according to the use of shape boundary or interior, and according to the type of the result. An overview of the most representative methods is presented. |
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M.A. Martinelli.
Pattern Recognition in Time-Series.
Technical Analysis in Stocks & Commodities,
1998.
Keywords:
Similarity Measures,
Sequential/Temporal Data.
| Abstract: |
The correltaion coefficient is a statistics that is used to measure "goodness-of-fit" in many curve-fitting procedures, such as least-squartes. Here we use it as an indicator of fit, or similarity, between a user-selected chart-pattern, and all segments of another chart having the same length. |
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Juiyao Pan,
Guilherme N. DeSouza,
and Avinash C. Kak.
FuzzyShell: A Large Scale Expert System Shell Using Fuzzy Logic for Uncertainty Reasoning.
TFS,
6(4):563--581,
1998.
| Abstract: |
There exist in the literature today many contributions dealing with the incorporation of fuzzy logic in expert systems. However, unfortunately, much of what has been proposed can only be applied to small-scale expert systems; that is, when the number of rules is in the dozens as opposed to in the hundreds. The more traditional (nonfuzzy) expert systems are able to cope with large numbers of rules by using Rete networks for maintaining matches of all the rules and all the facts. (A Rete network obviates the need to match the rules with the facts on every cycle of the inference engine.) In this paper, we present a more general Rete network that is particulary suitable for reasoning with fuzzy logic. The generalized Rete network consists of a cascade of three networks: the pattern network, the join network, and the evidence network. The first two layers are modified versions of similar layers for the traditional Rete networks and the last, the aggregation layer, is a new concept that allows fuzzyz evidence to be aggregated when fuzzy inferences are made about the same fuzzzy variable by different rules. |
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M. Ramze Rezaee,
B. P. F. Lelieveldt,
and J. H. C. Reiber.
A new cluster validity index for the fuzzy c-means.
PRL,
19:237--246,
1998.
Keywords:
Clustering,
Cluster Validity Measures,
Fuzzy c-Means.
| Abstract: |
In this paper a new cluster validity index is introduced, which assesses the average compactness and separation of fuzzy partitions generated by the fuzzy c-means algorithm. To compare the performance of this new index with a number of known validation indices, the fuzzy partitioning of two data sets was carried out. Our validation performed favorably in all studies, even in those where other validity indices failed to indicate the true number of clusters within each data set. |
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Magne Setnes,
Robert Babuska,
Uzay Kaymak,
and Hans R. van Nauta Lemke.
Similarity Measures in Fuzzy Rule Base Simplification.
SMCB,
28(3):376--386,
1998.
Keywords:
Similarity Measures,
Fuzzy Models.
| Abstract: |
In fuzzy rule-based models acquired from numerical data, redundancy may be present in the form of similar fuzzy sets that represent compatible concepts. This results in an unnecessarily complex and less transparent linguistic description of the system. By using a measure of similarity, a rule base simplification method is proposed that reduces the number of fuzzy sets in the model. Similar fuzzy sets are merged to create a common fuzzy set to replace them in the rule base. If the redundancy in the model is high, merging similar fuzzy sets might result in equal rules that also can be merged, thereby reducing the number of rules as well. The simplified rule base is computationally more efficient and linguistically more tractable. The approach has been successfully applied to fuzzy models of real world systems. |
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J. Shao.
Application of an artifial neural network to improve short-term road ice forecasts.
ESWA,
14:471--482,
1998.
Keywords:
Neural Networks.
| Abstract: |
This paper describes how a three-layer artificial neural network (NN) can be used to improve the accuracy of short-term (3-12 hours) automatic numerical prediction of road surface temperature, in order to cut winter road maintenance costs, reduce environmental damage from oversalting and provide safer roads for road users. In this paper, the training of the network is based on historical and preliminary meteorological parameters measured at an automatic roadside weather station, and the target of the training is hourly error of original numerical forecasts. The generalization of the trained network is then used to adjust the original model forecast. The effectiveness of the network in improving the accuracy of numerical model forecasts was tested at 39 sites in eight countries. Results of the tests show that the NN technique is able to reduce absolute error and root-mean-square error of temperature forecasts by 9.9-29\%, and increase the accuracy of frost/ice prediction. |
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Rosaria Silipo and Carlo Marchesi.
Articifial Neural Networks for automatic ECG analysis.
TSP,
46:1417--1425,
1998.
Keywords:
Classification,
Neural Networks,
Medical Applications.
| Abstract: |
The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks of a possible equipment integrating the most common features of the ECG analysis: arrhytmia, myocardial ischemia, chronic alterations. Several ANN architectures are implemented, tested, and compared with competing alternatives. Approach, structure, and learning algorithm of ANN were designed according to the features of each particular classification task. The trade-off between the time consuming training of ANN's and their performance is also explored. Data pre- and post-processing efforts on the system performance were critically tested. These efforts' crucial role on the production of the input space dimensions, on a more significant description of the input features, and on improving new or ambiguous event processing has been also documented. Finally, the algorithm assessment was done on data coming from all the currently available ECG databases. |
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