Clustering data streams has been a new research topic, recently emerged from many real data mining applications, and has attracted a lot of research attention. However, there is little work on clustering high-dimensional streaming text data. This paper combines an efficient online spherical k-means (OSKM) algorithm with an existing scalable clustering strategy to achieve fast and adaptive clust...

In traditional document clustering methods, a document is considered a bag of words. The fact that the words may be semantically relateda crucial information for clusteringis not taken into account. In this paper we describe a new method for generating feature vectors, using the semantic relations between the words in a sentence. The semantic relations are captured by the Universal Networking L...

Clustering is a widely studied data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In this chapter, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the...

Clustering is a widely studied data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In this chapter, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the...

We present a package which provides a general framework, including tools and algorithms, for text mining in R using the S4 class system. Using this package and the kernlab R package we explore the use of kernel methods for clustering (e.g., kernel k-means and spectral clustering) on a set of text documents, using string kernels. We compare these methods to a more traditional clustering techniqu...

Clustering is a widely studied data mining problem in the text documents. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In this paper, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the...

As the discovery of information from text corpora becomes more and more important there is a necessity to develop clustering algorithms designed for such a task. One of the most, successful approach to clustering is the density based methods. However due to the very high dimensionality of the data, these algorithms are not directly applicable. In this paper we demonstrate the need to suitably e...

An index or topic hierarchy of full-text documents can organize a domain and speed information retrieval. Traditional indexes, like the Library of Congress system or Dewey Decimal system, are generated by hand, updated infrequently, and applied inconsistently. With machine learning, they can be generated automatically, updated as new documents arrive, and applied consistently. Despite the appea...

Thematic organization of text is a natural practice of humans and a crucial task for today’s vast repositories. Clustering automates this by assessing the similarity between texts and organizing them accordingly, grouping like ones together and separating those with different topics. Clusters provide a comprehensive logical structure that facilitates exploration, search and interpretation of cu...

ABSTRACT: Based on clustering algorithm Affinity Propagation (AP) I present this paper a semisupervised text clustering algorithm, called Seeds Affinity Propagation (SAP). There are two main contributions in my approach: 1) a similarity metric that captures the structural information of texts, and 2) seed construction method to improve the semisupervised clustering process. To study the perform...