- 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.
- In other words, the clustering result depends on the order of picking up the item sets, which in turns depends on the greedy heuristic. This method does not follow a sequential order of selecting clusters. Instead, we assign documents to the best cluster.
- 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. Based on the Hierarchical Clustering Method, the usage of Expectation-Maximization (EM) algorithm in the Gaussian Mixture Model to count the parameters and make the two sub-clusters combined when their overlap is the largest is narrated.
- Experiments in both public data and document clustering data show that this approach can improve the efficiency of clustering and save computing time.
- HTML PARSER
- CUMMULATIVE DOCUMENT
- DOCUMENT SIMILARITY
- 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.
- 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.
- 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.
- TF = C / T
- IDF = D / DF.
- Clustering is a division of data into groups of similar objects.
- Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification.
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Clustering With Multi-Viewpoint Based Similarity Measure