博士论文集选参（一）。
Text Mining (TM for short) is a process to find out the potential value of text
knowledge, such as text information structure, model and pattern, etc. TM involves data
mining, pattern recognition, information retrieval, natural language processing and other
fields. Because text is the main way to store information, the importance of TM is
increasingly obvious.
In the present research to TM, traditional data mining methods still dominated.
However, with further research in TM, it faces more severe challenges to apply the
traditional data mining methods. These difficulties, such as the huge dimensions and
sparsity of text object, the high complexity of algorithm and the requirement of prior
knowledge and so on, have seriously hampered the development of TM.
In the final analysis, these problems in TM process are due to the uncertainty of
natural language. The uncertainty of natural language (especially text) comes from the
uncertainty of the human thinking in essence. It makes people to have a richer
understanding of spatial and cognitive abilities, but also brought a series of problems to
TM. Therefore, from the point of reducing the complexity of natural language, if we can
carry out the advanced innovation, which based on making full use of these existing
technologies, and find out a novel uncertainty artificial intelligence approach for TM, it
will greatly facilitate the rapid development of TM.
Cloud model is an important tool in the uncertain knowledge research. With the
efficient conversion function between qualitative and quantitative data, cloud model is
introduced to the key issues of TM. Our primary works are as follow.
(1) Cloud model theory expansion in TM.
The researches, which involve text knowledge representation, the physical space
conversion of the corresponding model and the similarity measures of the text concept,
have been carried out. The following three aspects are contained.
1) Text information table based on VSM model.
The information table in knowledge representation system is introduced to text
representation. On this basis, text system is expressed as text information table based on
VSM model.
2) Text information table conversion based on cloud model.
When cloud model is used to deal with the uncertainty relations between texts, itmusts be guaranteed that the values of every attribute are the same domain. That is to say, the different attribute values of text have the same physical meaning. But the
attributes of existing text information table have different inner meaning and their
values are vastly different. It needs to convert these attributes to the unified physical
space. Using probability statistical method, a text information table transformation
algorithm is proposed. Through this algorithm, the attributes of text information table
have been converted to the unified physical space and it reflects the probability
distribution of them.
3) Text similarity measure based on cloud similarity.
The cosine similarity is commonly used method to measure the similarity between
texts in text mining. Yet not matter what kind of similarity measure is based on the fact
that object properties must strict match. It will result in the lack of consideration of the
integrity of text object. Combined the overall distribution with the individual
characteristics of text object, a novel cloud similarity is proposed based on vector digital
characteristics of cloud, which is used to describe the overall text. By cloud similarity,
the similarity between texts is converted to the similarity between cloud vectors. It not
only improves the mining performance and can quickly identify the common features,
but also fully considers the randomness and fuzziness of the attribute values.
(2) Text feature automatic selection algorithm based on cloud model (named FAS).
Feature selection is an effective method for reducing the size of text feature space.
So far, some effective methods for feature selection have been developed. For the
purpose of acquiring the optimal number of features, these methods mainly depend on
observation or experience. In this paper, by combining the overall with the local
distribution of features in categories, a high performance algorithm for feature
automation selection (FAS) is proposed. By using FAS, the feature set can be obtained
automatically. Besides, it can effectively amend the distribution of features by using
cloud model theory. Analysis and open experimental results show the selected feature
set has fewer features and better classification performance than the existing methods.
(3) Text classifier based on cloud concept jumping up (named CCJU).
With the efficient conversion function between qualitative and quantitative data,
the concept extraction method of cloud model is applied to text classification. On the
basis of the conversion from text collection to text information table based VSM model,
the text qualitative concept, which is extraction from the same category, is jumping up.
According to compare the cloud similarity between the test text and each category, the
test text is assigned to the most similar category. Through the comparison among
different text classifiers based on different feature selection methods, it full proves that
CCJU not only has a strong ability to adapt to the different text features, the
classification performance is also better than the traditional classifiers.
(4) Rapid and unsupervised text clustering based on cloud similarity (named
CS-Means)
Aiming at the shortcomings of the existing text clustering algorithm, a rapid and
unsupervised text clustering based on cloud similarity is proposed. After text
pretreatment using FAS algorithm, it takes a gradual approach strategy to obtain the
optimal k (cluster number) value based on k-Means clustering algorithm. The process to
obtain k value is the automatic clustering process. In this period, the digital
characteristics of text cloud vector are extraction firstly. Next, the cloud similarity
degree is used to measure the similarity between texts. The algorithm not only avoids
the difficulties which bring by the huge dimensions and sparsity of text objects, but also
retains the high performance of k-Means. At the same time, the gradual approach
strategy also solves the problem which is how to assign the cluster numbers. So, the
clustering results are more meet the characteristic of text distribution.
Keywords：text mining, cloud model, text cloud similarity, text feature selection, text
classification and clustering
