Clustering is related to data mining for information retrieval. Relevant information is retrieved quickly while doing the clustering of documents. It organizes the documents into groups; each group contains the documents of similar type content. Different clustering algorithms are used for clustering the documents such as partitioned clustering (K-means Clustering) and Hierarchical Clustering (...

Semantic clustering is an important and challenge task for content-based image database management. This paper proposes a semantic clustering learning technique, which collects the relevance feedback image retrieval transaction and uses hypergraph to represent images correlation ship, then obtains the semantic clusters by hypergraph partitioning. Experiments show that it is efficient and simple.

Organizing textual documents by categorizing them is important and beneficial for information retrieval; but when it comes to clustering documents containing a huge number of terms, the task become challenged. Therefore, selecting effective features is essential for reducing the feature space dimensionality and improving the clustering performances. While numerous methods have been developed fo...

Document clustering is a fundamental task of text mining, by which efficient organization, navigation, summarization and retrieval of documents can be achieved. The clustering of documents presents difficult challenges due to the sparsity and the high dimensionality of text data, and to the complex semantics of the natural language. Subspace clustering is an extension of traditional clustering ...

The order in which participants choose to recall words from a studied list of randomly selected words provides insights into how memories of the words are represented, organised, and retrieved. One pervasive finding is that when a pair of semantically related words (e.g., "cat" and "dog") is embedded in the studied list, the related words are often recalled successively. This tendency to succes...

The present study examined how the presentation format of the study list influences younger and older adults' semantic clustering. Spontaneous clustering did not differ between age groups or between an individual-words (presentation of individual study words in consecution) and a whole-list (presentation of the whole study list at once for the same total duration) presentation format in 132 you...

In traditional text clustering, documents appear terms frequency without considering the semantic information of each document (i.e., vector model). The property of vector model may be incorrectly classified documents into different clusters when documents of same cluster lack the shared terms. Recently, to overcome this problem uses knowledge based approaches. However, these approaches have an...

It is a critical problem of P2P network about how to efficiently and accurately search resources on P2P network. This thesis mainly starts from improving query efficiency to establish an intelligent search framework. On the basis of Gnutella-flooding search technology, it applies theories of semantic ontology search combined with semantic hash routing table technology and searches accurate answ...

Handcrafting semantic classes is a difficult and time-consuming job, and depends on human interpretation. Possible machine learning techniques would be much faster, and do not rely on interpretation, because they stick to the data. The goal of this research is to present some machine learning techniques that make it possible to achieve an automatic clustering of Dutch words. More particularly, ...

This paper proposes a new algorithm, called Semantic Suffix Tree Clustering (SSTC), to cluster web search results containing semantic similarities. The distinctive methodology of the SSTC algorithm is that it simultaneously constructs the semantic suffix tree through an on-depth and on-breadth pass by using semantic similarity and string matching. The semantic similarity is derived from the Wor...