07-04-2012, 01:00 PM
Clustering with Multi-Viewpoint based Similarity Measure
Clustering with Multi-Viewpoint.doc (Size: 325 KB / Downloads: 98)
ABSTRACT:
All clustering methods have to assume some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. In this paper, we introduce a novel multi-viewpoint based similarity measure and two related clustering methods. The major difference between a traditional dissimilarity/similarity measure and ours is that the former uses only a single viewpoint, which is the origin, while the latter utilizes many different viewpoints, which are objects assumed to not be in the same cluster with the two objects being measured. Using multiple viewpoints, more informative assessment of similarity could be achieved.
EXISTING SYSTEMS
• Clustering is one of the most interesting and important topics in data mining. The aim of clustering is to find intrinsic structures in data, and organize them into meaningful subgroups for further study and analysis. There have been many clustering algorithms published every year.
• Existing Systems greedily picks the next frequent item set which represent the next cluster to minimize the overlapping between the documents that contain both the item set and some remaining item sets.
PROPOSED SYSTEM
• The main work is to develop a novel hierarchal algorithm for document clustering which provides maximum efficiency and performance.
• It is particularly focused in studying and making use of cluster overlapping phenomenon to design cluster merging criteria. Proposing a new way to compute the overlap rate in order to improve time efficiency and “the veracity” is mainly concentrated.
MODULE DESCRIPTION:
• Parsing is the first step done when the document enters the process state.
• Parsing is defined as the separation or identification of meta tags in a HTML document.
• Here, the raw HTML file is read and it is parsed through all the nodes in the tree structure.
Cumulative Document
• The cumulative document is the sum of all the documents, containing meta-tags from all the documents.
• We find the references (to other pages) in the input base document and read other documents and then find references in them and so on.
• Thus in all the documents their meta-tags are identified, starting from the base document.
Document Similarity
• The similarity between two documents is found by the cosine-similarity measure technique.
• The weights in the cosine-similarity are found from the TF-IDF measure between the phrases (meta-tags) of the two documents.
• This is done by computing the term weights involved.