Tatiane M. Nogueira

On the use of fuzzy rules to text document classification

This work presents the integration of a fuzzy method and text mining to obtain an approach that enables the text documents classification to be closer to the user needs. The aim of this work is to develop a mechanism to reduce the high dimensionality of the attribute-value matrix obtained from the documents and, with this, to manage the imprecision and uncertainty using fuzzy rules to classify text documents. Some experiments have been run using different domains in order to validate the proposed approach and to compare the results with the ones obtained with the Ibk, J48, Naive Bayes and OneR classification methods. The advantages of the method, the experiments and the results obtained are discussed.

Fuzzy cluster descriptor extraction for flexible organization of documents

The text mining process and its set of techniques have been widely used in order to look for new knowledge in textual documents, which can be recovered by information retrieval systems. There is a variety of methods developed to automatically organize documents based on the knowledge extracted from their content. The management of imprecision and uncertainty is very important to improve these methods. Therefore, this work proposes a method to manage imprecision and uncertainty in document organization, by clustering the documents in fuzzy clusters and extracting cluster descriptors. The proposed method was evaluated and the obtained results showed that this is a promising approach to deal with the problem of imprecision and uncertainty when organizing textual documents.

A Meta-ontology Approach for Representing Vague Linguistic Terms and Fuzzy Rules for Classification in Ontologies

Ontologies have been successfully employed in applications that require semantic information processing. However, traditional ontologies are less suitable to express fuzzy or vague information, which often occurs in human vocabulary as well as in several application domains. In order to deal with such restriction, concepts from fuzzy set theory should be incorporated into ontologies so that it is possible to represent and reason over fuzzy or vague knowledge. In this context, this paper proposes a meta-ontology approach for representing fuzzy ontologies covering fuzzy properties, fuzzy rules, and fuzzy reasoning methods such as classical and general fuzzy reasoning, aiming to support the classification of new individuals based on rules containing fuzzy properties.

Flexible document organization: Comparing fuzzy and possibilistic approaches

System flexibility means the ability of a system to manage imprecise and/or uncertain information. A lot of commercially available Information Retrieval Systems (IRS) address this issue at the level of query formulation. Another way to make the flexibility of an IRS possible is by means of the flexible organization of documents. Such organization can be carried out using clustering algorithms by which documents can be automatically organized in multiple clusters simultaneously. Fuzzy and possibilistic clustering algorithms are examples of methods by which documents can belong to more than one cluster simultaneously with different membership degrees. The interpretation of these membership degrees can be used to quantify the compatibility of a document with a particular topic. The topics are represented by clusters and the clusters are identified by one or more descriptors extracted by a proposed method. We aim to investigate if the performance of each clustering algorithm can affect the extraction of meaningful overlapping cluster descriptors. Experiments were carried using well-known collections of documents and the predictive power of the descriptors extracted from both fuzzy and possibilistic document clustering was evaluated. The results prove that descriptors extracted after both fuzzy and possibilistic clustering are effective and can improve the flexible organization of documents.

Fuzzy cluster descriptors improve flexible organization of documents

System flexibility means the ability of a system to manage imprecise and/or uncertain information. There are two ways to address the Information Retrieval Systems (IRS) flexibility: through methods that improve the query formulation and through methods that improve the document organization. Since the query formulation has obtained more attention in retrieval process, we aim to investigate the flexibility in document organization. When a document organization is carried out using fuzzy clustering, the documents can belong to more than one cluster simultaneously with different membership degrees, allowing the management of imprecise and/or uncertain information in the collection organization. Clusters represent topics and are identified by one or more descriptors. In this work we use an unsupervised method to extract cluster descriptors for a specific database and investigate whether the quality of the fuzzy cluster descriptors improves the flexible organization of documents.

Fuzz-Onto: A meta-ontology for representing fuzzy elements and supporting fuzzy classification rules

Ontologies have been employed in applications that require semantic information representation and processing. However, traditional ontologies are not suitable to express fuzzy or vague information, which often occurs in human vocabulary as well as in several application domains. To deal with this limitation, concepts from the Fuzzy Set Theory can be incorporated into ontologies making it possible to represent and reason over fuzzy or vague knowledge. In this context, this paper proposes Fuzz-Onto, a meta-ontology for representing fuzzy ontologies which, so far, models fuzzy concepts, fuzzy relationships and fuzzy properties. In particular, the representation of fuzzy properties and linguistic terms makes it possible to combine fuzzy modeling in ontologies with existing fuzzy rule-based classification methods. The paper also presents a case study in the knowledge domain of scientific documents as an instantiation of the modeling-inference articulation.

Flexible document organization by mixing fuzzy and possibilistic clustering algorithms

A powerful and flexible organization of documents can be obtained by mixing fuzzy and possibilistic clustering. In such organization, documents can belong to more than one cluster simultaneously with different compatibility degrees. Clusters represent topics, which are identified by one or more descriptors extracted by a proposed method. In this manuscript, we investigated whether or not the descriptors extracted after applying possibilistic fuzzy clustering improve the flexible organization of documents. Experiments were carried out on real-world document collections and we evaluated the ability of descriptors to capture the essential information in every dataset. Results have shown the effectiveness of extracting possibilistic fuzzy cluster descriptors, improving the flexible organization of documents.

Applying classification methods to model standby power consumption in the Internet of Things

This paper presents an approach that combines Internet of Things (IoT) technologies and classification methods to improve efficient usage of power consumption. We focused on energy use of electronic devices on standby mode, which represent from 5 to 26% of power consumption in a home. The proposed approach aims at predicting situation in which devices on standby can be turned off, reducing power consumption. In summary, our approach uses motion and current sensors connected to an IoT infrastructure to build a profile about the presence of people at home. Results obtained from our approach present a reduction of the electric energy consumption by applying Machine Learning methods on Internet of Things scenarios.

On Fuzzy Cluster Validity Indexes for High Dimensional Feature Space

Fuzzy document clustering aims at automatically organizing related documents into clusters in a flexible way. At this context, the topics identification addressed by documents in every cluster is performed by automatically discovering cluster descriptors, which are relevant terms present in these documents. Since documents are represented by a high-dimensional feature space, the extraction of good descriptors is a big problem to be solved. This problem is even bigger using fuzzy clustering, since the same descriptor can be representative for more than one cluster. Moreover, it is well-known that the Fuzzy C-Means clustering algorithm is also affected by documents dimensionality and the choice of correct partition of a given document collection into clusters is still a challenging problem. In order to overcome this drawback, we have investigated the most common fuzzy clustering validity indexes to validate the organization of data with high dimensional feature space, since they are commonly used to evaluate fuzzy clusters from low dimensional data sets.