# Indexing in Search Engines based on Pipelining Architecture using Single Link HAC

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2 an initial stage and an iterative stage. The initial stage involves defining K initial centroids, one for each cluster. These centroids must be selected carefully because of differing initial centroids causes differing results. One policy for selecting the initial centroids is to place them as far as possible from each other. The second, iterative stage repeats the assignment of each data point to the nearest centroid and K new centroids are recalculated according to the new assignments. This iteration stops when a certain criterion is met. This is a effective heuristic method. The number of iterations required may be large. Another work proposed was the Reordering algorithm [10] reordering of a collection of documents D is a bijective function _: {1,...,N}! {1,...,N} that maps each doc id i into a new integer _ (i). Moreover, with _ (D) = D_(1),D_(2),...,D_(N). It will indicate the permutation of D resulting from the application of function. This algorithm is not effective in clustering the most similar documents. The biggest document may not have similarity with any of the documents but still it is taken as the representative of the cluster. In this technology the number of clusters is unknown. However, two documents are classified to the same cluster if the similarity between them is below a specified threshold. This threshold is defined by the user before the algorithm starts. It is easy to see that if the threshold is small; all the elements will get assigned to different clusters If the threshold is large, the elements may get assigned to just one cluster. Thus the algorithm is sensitive to specification of threshold. This paper the proposed algorithm has to overcome the shortcomings of existing algorithm. It produces a better clustering of documents in the cluster. This algorithm selects the two clusters with minimum distance between them and merges to form a new cluster. Next it will merge with the cluster having shortest distance between them. 3. Proposed Approach The Pipelining architecture will create the inverted index for every cluster. As a result a hierarchy of inverted indexes is created. The inverted index store all common word in the cluster at one level and produce the result for the next higher level, so at higher level it have less word in index. The core of indexer is the index building process that executes on each cluster. The Corpus is the collection of data on the web that is needed to be indexed. During the clustering phase it will take the document from the corpus and starts clustering process by using single link agglomerative clustering. In single link clustering clusters are merged at each stage by Single shortest distance between them. During each iteration after the cluster x and cluster y are merged the distance between the new cluster say n and another cluster say z is given by dnz= min (dxz, dyz) where dnz denotes the distance between the two closest members of cluster n and z. If the cluster n and z were to be merged. Then for any object in the resulting cluster the distance to its nearest neighbor would be at most dnz. 3.1 The Pipelined Indexer Design Corpus Single Link HAC cluster C C 1 C 2 Tokenizer Linguistic Modules Indexer Inverted Index Disk Searcher Figure 2 Pipelined Architecture of indexing 13

4 , , ,3 1,3,4,5,6,7,8,2 1,3,4,5,6,7,8 2 4,5,6,7, ,5, ,5, ,5,6,8 8 4,5,6 4 5, ,5,6, ,5,6, ,5,6,7, ,5,6,7,8 1,3,4,5,6,7,8 2 1,3,4,5,6,7, Figure 5 Similarity matrices Now according to algorithm it will take two clusters and make one on the basis of similarity. In the last Iteration 1,3,4,5,6,7,8 is the mega cluster and 2 is a alone cluster. It will put to the next level according to algorithm. Now it will create a hierarchy shown as in the figure 6. Figure 6 Hierarchies of Seven Documents Performance Analysis 1-After applying the single link Hierarchical agglomerative clustering a hierarchy of documents is generated. Each cluster is has its own inverted index. So if the user generates a query it will corresponds to the lower level of cluster because at lower level the cluster have few terms in it as compare to the higher level. So it takes less time in order to execute the query. 2. In single link HAC, there is no need to refine the value of k for reordering as in the case of K means, which is quite time consuming. 3. K means is a simple heuristic technique which is quite time consuming, and there is no fixed dead end to stop the iterations. It may be faster but it could not guarantee the result. 4. As it based on the pipelining architecture of indexing. it will consume less time because as one thread will do the clustering for one level another thread will create thread inverted index for the other level. 5. It also saves the space because it used to store only those terms which are common at each level so size of the inverted index will also reduce as we go up in the hierarchy. The performance can be evaluated by the graph shown in the figure 7. The graph is plotted between the execution time and the number of documents.. 15

5 7. FUTURE WORK The future work can be done for making inverted index better or can apply another approach in architecture. We can also rank our results in proper way in order to reduce the time taken in searching the data. Number of documents (in thousands ) Figure 7 The graph is evaluating the performance with pipeline and without pipeline 6. CONCLUSION The proposed pipeline architecture of indexing has four logical phases clustering, loading, processing, storing. It takes less time to index the documents. This architecture is generates a hierarchy of clusters of at the same time it will take the first level of cluster and start processing and create the inverted index. Similarly for the next level it will do inverted index. When the user makes a query it will go to higher index which will have fewer words. By having the less word in index time will be saved for searching the word. For each cluster there is one inverted index. If searcher will match for other keyword which are not in higher index then it will come down to lower index. This will take less time in searching for the documents of clusters. So it is efficient in time and space saving and produce faster search. 8. REFERENCES [1] Grossman, Frieder, Goharian. IR Basics of Inverted Index Verified Dec [2] Anand Rajaraman,Jeffrey D.Ullman, Mining of massive data sets Stanford University [3] J. B. MacQueen (1967): "Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability", Berkeley, University of California Press, 1: [4] Athman Bouguettaya "On Line Clustering", IEEE Transaction on Knowledge and Data Engineering Volume 8, No. 2, April [5] Sergey Melnik, Sriram Raghavan, Beverly Yang, and Hector Garcia-Molina. Building a Distributed Full Text Index for the Web. In World Wide Web, pages ,2001 [6] Parul Gupta, Dr. A.K. Sharma, Hierarchical Clustering based Indexing in Search Engines, communicated to International Journal of Information and Communication Technology. [7] Sanjiv K. Bhatia. Adaptive K-Means Clustering American Association for Artificial Intelligence, [8] Oren zamir Web document Clustering department of Computer science and engineering,university of Washington.2007 [9] Yi Zhang and Tao li A Framework for evaluating and organizing document clustering through visualization published by ACM, USA. 16

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