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Text Data Clustering
Leixner, Petr ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
Process of text data clustering can be used to analysis, navigation and structure large sets of texts or hypertext documents. The basic idea is to group the documents into a set of clusters on the basis of their similarity. The well-known methods of text clustering, however, do not really solve the specific problems of text clustering like high dimensionality of the input data, very large size of the databases and understandability of the cluster description. This work deals with mentioned problems and describes the modern method of text data clustering based on the use of frequent term sets, which tries to solve deficiencies of other clustering methods.
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Stemming Methods Used in Text Mining
Adámek, Tomáš ; Chmelař, Petr (referee) ; Bartík, Vladimír (advisor)
The main theme of this master's thesis is a description of text mining. This document is specialized to English texts and their automatic data preprocessing. The main part of this thesis analyses various stemming algorithms (Lovins, Porter and Paice/Husk). Stemming is a procedure for automatic conflating semantically related terms together via the use of rule sets. Next part of this thesis describes design of an application for various types of stemming algorithms. Application is based on the Java platform with using of graphic library Swing and MVC architecture. Next chapter contains description of implementation of the application and stemming algorithms. In the last part of this master's thesis experiments with stemming algorithms and comparing the algorithm from viewpoint to the results of classification the text are described.
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Determination of basic form of words
Šanda, Pavel ; Burget, Radim (referee) ; Karásek, Jan (advisor)
Lemmatization is an important preprocessing step for many applications of text mining. Lemmatization process is similar to the stemming process, with the difference that determines not only the word stem, but it´s trying to determines the basic form of the word using the methods Brute Force and Suffix Stripping. The main aim of this paper is to present methods for algorithmic improvements Czech lemmatization. The created training set of data are content of this paper and can be freely used for student and academic works dealing with similar problematics.
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Metadata Extraction from Scientific Papers
Lokaj, Tomáš ; Dytrych, Jaroslav (referee) ; Otrusina, Lubomír (advisor)
This work deals with the Metadata Extraction from Scienti c Papers. There is generally described issue of information extraction, focusing on the processing of text documents. There is also presented programme clanky2meta.py designed to search for relevant information in scienti c publication, created by the author. At the end of this work is a comparsion of systems dealing with the same issue, especially with the CiteSeerX system.
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Textual Data Clustering Methods
Miloš, Roman ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
Clustering of text data is one of tasks of text mining. It divides documents into the different categories that are based on their similarities. These categories help to easily search in the documents. This thesis describes the current methods that are used for the text document clustering. From these methods we chose Simultaneous keyword identification and clustering of text documents (SKWIC). It should achieve better results than the standard clustering algorithms such as k-means. There is designed and implemented an application for this algorithm. In the end, we compare SKWIC with a k-means algorithm.
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Processing of User Reviews
Cihlářová, Dita ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
Very often, people buy goods on the Internet that they can not see and try. They therefore rely on reviews of other customers. However, there may be too many reviews for a human to handle them quickly and comfortably. The aim of this work is to offer an application that can recognize in Czech reviews what features of a product are most commented and whether the commentary is positive or negative. The results can save a lot of time for e-shop customers and provide interesting feedback to the manufacturers of the products.
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Processing of User Reviews
Cihlářová, Dita ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
Very often, people buy goods on the Internet that they can not see and try. They therefore rely on reviews of other customers. However, there may be too many reviews for a human to handle them quickly and comfortably. The aim of this work is to offer an application that can recognize in Czech reviews what features of a product are most commented and whether the commentary is positive or negative. The results can save a lot of time for e-shop customers and provide interesting feedback to the manufacturers of the products.
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Metadata Extraction from Scientific Papers
Lokaj, Tomáš ; Dytrych, Jaroslav (referee) ; Otrusina, Lubomír (advisor)
This work deals with the Metadata Extraction from Scienti c Papers. There is generally described issue of information extraction, focusing on the processing of text documents. There is also presented programme clanky2meta.py designed to search for relevant information in scienti c publication, created by the author. At the end of this work is a comparsion of systems dealing with the same issue, especially with the CiteSeerX system.
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Text Data Clustering
Leixner, Petr ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
Process of text data clustering can be used to analysis, navigation and structure large sets of texts or hypertext documents. The basic idea is to group the documents into a set of clusters on the basis of their similarity. The well-known methods of text clustering, however, do not really solve the specific problems of text clustering like high dimensionality of the input data, very large size of the databases and understandability of the cluster description. This work deals with mentioned problems and describes the modern method of text data clustering based on the use of frequent term sets, which tries to solve deficiencies of other clustering methods.
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