A modified fuzzy ART for soft document clustering

Document clustering has been an important research area in recent years. However, most work on this subject has focused on batch processing in a static environment. For real world applications, online and incremental processing of highly dynamic data is required. Adaptive resonance theory (ART) neural networks possess several interesting properties that make them appealing as a potential solution to this problem. In this paper, we present preliminary experimental results that examine ART text clustering under several situations characteristic of real-life applications. We also compare our present results with work we have conducted previously on the batch static case, hence determining how clustering quality is affected by incremental processing.

While learning algorithms have been used for astronomical data analysis, the vast majority of those algorithms have used supervised learning. In a continuation of the work described in Young et ah [18] we examine the use of unsupervised learning for this task with two types of Adaptive Resonance Theory (ART) neural networks. Using synthetic astronomical data from SkyMaker[2], [3] which was designed to mimic the dynamic range of the CTI-[14] telescope, we compared the ability of the ART-1 neural network[4] and the ART-1 neural network with category theoretic modiflcation[9], [11] to detect regions of interest and to characterize noise. We show a difference in the geometries of the templates created by each architecture. We also show an analysis of the two architectures over a range of parameter settings. The results provided show that ART neural networks and unsupervised learning algorithms in general should not be overlooked for astronomical data analysis.

A new method of texture classification comprising two processing stages, namely a low-level evolutionary feature extrac- tion based on Gabor wavelets and a high-level neural network based pattern recognition, is proposed. The design of these stages is motivated by the processes involved in the human visual system: low-level receptors responsible for early vision processing and the high-level cognition. Gabor wavelets are used as extractors of ''low- level'' features that feed the feature-adaptive adaptive resonance theory (ART) neural network acting as a high-level ''cognitive sys- tem.'' The novelty of the model developed in this paper lies in the use of a self-organizing input layer to the fuzzy ART. Evaluation of the model is performed by using natural textures, and results ob- tained show that the developed model is capable of performing the texture recognition task effectively. Applications of the developed model include the study of artificial vision systems motivated by the human visual system model. © 1997 SPIE and IS&T. (S1017-9909(97)00403-0)

The case retrieval model based on neural network is presented to enhance the efficiency and quality of retrieving case in the case-based reasoning system of the motorcycle intelligent design. In the retrieval model, the adaptive resonance theory neural network was used to dynamically cluster the cases in the case base to narrow the searching range. The back propagation neural network was applied to memory the index of cases to retrieve quickly the similar case from the narrowed case base. Thus the efficiency and quality of retrieving case are improved. Finally, an example of the plan selection of motorcycle general design was given. Its result was contrasted with that of case retrieval based on the nearest neighbor method to demonstrate the effectives of the case retrieval model. The research shows that it is practicable and effective using the adaptive resonance theory and BP neural network to modeling the reasoning mechanism.

Document Clustering - Concepts, Metrics and AlgorithmsDocument clustering, which is also refered to astext clustering, is a technique of unsupervised document organisation. Text clustering is used to group documents into subsets that consist of texts that are similar to each orher. These subsets are called clusters. Document clustering algorithms are widely used in web searching engines to produce results relevant to a query. An example of practical use of those techniques are Yahoo! hierarchies of documents [1]. Another application of document clustering is browsing which is defined as searching session without well specific goal. The browsing techniques heavily relies on document clustering. In this article we examine the most important concepts related to document clustering. Besides the algorithms we present comprehensive discussion about representation of documents, calculation of similarity between documents and evaluation of clusters quality.

A digital VLSI circuit design for an adaptive resonance theory (ART) neural network architecture, called the augmented ART-1 neural network (AARTI-NN) is presented. An ‘axon-synapse-tree’ structure is used to realize the activities of the short-term memories and reset subsystem. The long-term memory traces are implemented using NMOS transmission gales. PSpice circuit simulation was carried out to verify the design of a prototype, seven-node AARTI-NN. A clock-controlled delay element is included in the simulation to illustrate the functionality of the AARTI-NN. It is shown that the AARTI-NN node selection activities simulated from the circuit designed are identical to those described by the coupled differential equations governing the AARTI-NN.

Document Clustering is one of the popular techniques that can unveil inherent structure in the underlying data. Two successful models of unsupervised neural networks, Self-Organizing Map (SOM) and Adaptive Resonance Theory (ART) have shown promising results in this task. The high dimensionality of the data has always been a challenging problem in document clustering. It is common to overcome this problem using dimension reduction methods. In this paper, we propose a new two-level neural network based document clustering architecture that can be used for high dimensional data. Our solution is to use SOM in the first level as a dimension reduction method to produce multiple output clusters, then use ART in the second level to produce the final clusters using the reduced vector space. The experimental results of clustering documents from the RETURES corpus using our proposed architecture show an improvement in the clustering performance evaluated using the entropy and the f_measure.KeywordsVector Space ModelDocument ClusterFinal ClusterAdaptive Resonance TheoryDimension Reduction MethodThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Availability of large full-text document collections in electronic form has created a need for tools and techniques that assist users in organizing these collections. Document clustering is one of the popular methods used for this purpose. The Self-organizing map (SOM), an unsupervised algorithm for clustering and topographic mapping, has shown promising results in this task. Most of the existing SOM techniques rely on a “bag of words” document representation. Each word in the document is considered as a separate feature, ignoring the word order. In this chapter we investigate the use of phrases rather than words as document features for document clustering. We present a phrase grammar extraction technique, and use the extracted phrases as features in two different document clustering algorithms, self-organizing map (SOM) and hierarchical self-organizing map (HSOM). We present results of clustering documents from the REUTERS corpus and show an improvement in the clustering performance evaluated using the entropy and F-measure.

Clustering is a technique that group data together based on their similarity and apart based on their dissimilarity. When this technique is applied to documents and the terms within these documents retrieval of similar documents become easy and efficient. Document clustering is being researched and utilized for many years but is yet far from being optimal. To study and analyze different document clustering algorithm, a theoretical literature review and analysis was performed and the results are presented in this paper. This paper comprises of theoretical review of papers. 95 papers were identified and out of these 30 were selected. Various techniques or algorithms and modifications to previous algorithms proposed for document clustering by various researchers are compiled and presented with the intent that it will aid the researchers in finding out the current and future scope of research in information retrieval systems and document clustering technologies.

In this paper, we present an anomaly detection system that is able to detect time-related anomalies by using a wireless sensor network and a mobile robot. The sensor network uses an unsupervised fuzzy adaptive resonance theory (ART) neural network to learn and detect intruders in a previously unknown environment. Upon the detection of an intruder, a mobile robot travels to the position where the intruder is detected to investigate by using its camera. The wireless sensor network uses a hierarchical communication/learning structure, where the mobile robot is the root node of the tree. Our fuzzy ART network is based on Kulakov and Davcevpsilas implementation (Kulakov and Davcev, 2005). However, we enhance their work by extending the fuzzy ART neural network with a Markov model to learn a time series and detect time-related anomalies. Finally, a mobile robot is employed to verify whether the detected anomalies were caused by intruders. The proposed architecture is tested on physical hardware. Our results show that our enhanced detection system with mobile robot verification has a higher accuracy and lower false alarm rate than the original fuzzy ART system.

IMI2: A fuzzy clustering validity index for multiple imbalanced clusters

This paper presents a novel document clustering algorithm that represents documents as a time series of words. Document clustering is very important due to the fact that it permits us to group them based on some certain criteria, especially nowadays when a large number of articles are available. The timed series representation of the document instead of the vector model permits us to consider a new algorithm for the computation of the distance between documents: dynamic time warping. This novel representation together with the dynamic time warping algorithm represents the foundation for computing the similarity and the clustering of the documents. The clustering algorithm used is hierarchical clustering. This novel clustering method of texts is applied on named entities and on the parts of speech of the words that compose the documents. As test data we are using the Reuters corpus of newspaper articles.

This study presents a simplified fuzzy ARTMAP (SFAM) for different classification applications. The proposed SFAM model is synergy of fuzzy logic and adaptive resonance theory (ART) neural networks. SFAM is supervised network consisting of two layers (Fuzzy ART and Inter ART) that build constant classification groups in answer to series of input patterns. Fuzzy ART layer takes a series of input patterns and relate them to output classes. Inter ART layer functions in such a way that it raises the vigilance parameter of fuzzy ART layer. By combining this two layers, SFAM is capable to perform classification very efficiently and giving very high performances. Lastly, the SFAM model is applied to different simulations. The simulation results obtained for the three different datasets: Iris, Wisconsin breast cancer and wine dataset prove that SFAM model has better performance results than other models for these classification applications.

Group incremental adaptive clustering based on neural network and rough set theory for crime report categorization

Clustering documents enable the user to have a good overall view of the information contained in the documents. Most classical clustering algorithms assign each data to exactly one cluster, thus forming a crisp partition of the given data, but fuzzy clustering allows for degrees of membership, to which a data belongs to different clusters. In this system, documents are clustered by using fuzzy c-means (FCM) clustering algorithm. FCM clustering is one of well-know unsupervised clustering techniques. However FCM algorithm requires the user to pre-define the number of clusters and different values of clusters corresponds to different fuzzy partitions. So the validation of clustering result is needed. PBM index and F-measure are used for cluster validity.