Enhanced Clustering Technique for Search Engine Results using K-Algorithm (original) (raw)
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An intelligent clustering approach for improving search result of a website
International Journal of Advanced Intelligence Paradigms, 2019
These days, the internet has become part of our life, and thus web data usage has increased tremendously. We proposed a model that will improve the search result using clustering approach. Clustering is being used to group the data into the relevant folder so that accessing of information will be fast. The K-means clustering algorithm is very efficient in terms of speed and is suitable for large dataset. However, K-means algorithm has some drawbacks, such as the number of clusters need to be defined in starting itself, initialisation affects the output, and it often gets stuck to local optima. We proposed a hybrid model that determines the number of clusters itself and gives global optimal result. The number which has been obtained is passed as a parameter for the K-means. Thus, our novel hybrid model integrates the features of K-means and genetic algorithm. The model will have the best characteristics of K-means and genetic algorithm, and overcomes the drawbacks of K-means and genetic algorithm.
Clustering Web Search Results-A Review
The rapid growth of the Internet has made the Web a popular place for collecting information. Today, Internet user access billions of web pages online using search engines. Information in the Web comes from many sources, including websites of companies, organizations, communications and personal homepages, etc. Effective representation of Web search results remains an open problem in the Information Retrieval (IR) community. Web search result clustering has been emerged as a method which overcomes these drawbacks of conventional information retrieval (IR) community. It is the clustering of results returned by the search engines into meaningful, thematic groups. This paper gives issues that must be addressed in the development of a Web clustering engine and categorizes various techniques that have been used in clustering of web search results.
IJERT-Clustering of Web Search Results using Hybrid Algorithm
International Journal of Engineering Research and Technology (IJERT), 2016
https://www.ijert.org/clustering-of-web-search-results-using-hybrid-algorithm https://www.ijert.org/research/clustering-of-web-search-results-using-hybrid-algorithm-IJERTV4IS120183.pdf Clustering the web search has become a very fascinating research area among scientific and academic associations involved in information retrieval. It is also knows as Web Clustering Engines, appeal to increase the description of documents presented to the user for review, while decreasing the time spent reviewing them. Many algorithms for web document clustering already exist, but conclusions show there is room for more algorithms. Our Project works on providing concise information on an ambiguous search. This allows the user to gain precise information faster and reduces the time spent on looking through thousands of pages for simple information. The information obtained will be segmented, sorted and irrelevant information will be avoided.
Clustering of Web Search Results using Hybrid Algorithm
International Journal of Engineering Research and, 2015
Clustering the web search has become a very fascinating research area among scientific and academic associations involved in information retrieval. It is also knows as Web Clustering Engines, appeal to increase the description of documents presented to the user for review, while decreasing the time spent reviewing them. Many algorithms for web document clustering already exist, but conclusions show there is room for more algorithms. Our Project works on providing concise information on an ambiguous search. This allows the user to gain precise information faster and reduces the time spent on looking through thousands of pages for simple information. The information obtained will be segmented, sorted and irrelevant information will be avoided.
A Survey On Web Search Result Clustering And Engines
2013
Now a days World Wide Web is a very large distributed digital information space. The ability to search and retrieve information from the Web efficiently and effectively is an enabling technology for realizing its full potential. Current search tools retrieve too many documents, of which only a small fraction are relevant to the user query. Web clustering engines organize search results by topic, thus offering a complementary view to the flat-ranked list returned by conventional search engines. This paper highlights the main characteristics of a number of existing Web clustering engines and also discuss how to evaluate their retrieval performance.
The Survey-Web Search Results Clustering and Basic of Clustering Process
2012
As the number of documents on the web has proliferated, the low precision of conventional web search engines and the flat ranked list presentation make it difficult for users to locate specific information of interest. Grouping web search results into a hierarchy of topics provides an alternative to the flat ranked list and facilitates searching and browsing. In this paper, we have a brief survey of previous work on web search results clustering and basic steps of web search results clustering.
A Relative Study on Search Results Clustering
The performance of the web search engines could be improved by properly clustering the search result documents.. Most of the users are not able to give the appropriate query to get what exactly they wanted to retrieve. So the search engine will retrieve a massive list of data , which are ranked by the page rank algorithm(7) or relevancy algorithm or human judgment algorithm.
Top-K Search Query Grouping using SOM Clustering for Search Engine
International Journal of Computer Applications, 2015
Clustering is important task for any recommendation system. Clustering method suggested by many researchers for search engine optimization. Search engine help user for better searching by user's query recommendation. Clustering is helpful for finding actual relation between different queries which are not same as they seems. But do clustering of user query is also a difficult task because of user enters lots of type and varying queries. Many time these queries may very short to get their real meaning and also can generate different meanings. Any single query may have various meaning on other hand many different query words may have common meaning for searching contents. Lots of clustering methods are given in last decades for search engine optimization but these methods unable to proper utilization various information hidden in user query log. This paper gives a novel clustering approach based on to identify query similarity and apply SOM clustering for effective clustering results. We propose a novel similarity matrix for user queries by uses of URL clicked by user trough searching results. Text similarity and time similarity are also measure for calculating similarity between two queries. This method shows good results within clustering performance to compare with other existing methods.
2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013
The clustering of web search has become a very interesting research area among academic and scientific communities involved in information retrieval. Clustering of web search result systems, also called Web Clustering Engines, seek to increase the coverage of documents presented for the user to review, while reducing the time spent reviewing them. Several algorithms for web document clustering already exist, but results show there is room for more to be done. This paper introduces a new description-centric algorithm for clustering of web results called IFCWR. IFCWR initially selects a maximum estimated number of clusters using Forgy's strategy, then it iteratively merges clusters until results cannot be improved. Every merge operation implies the execution of Fuzzy C-Means for clustering results of web search and the calculus of Bayesian Information Criterion for automatically evaluating the best solution and number of clusters. IFCWR was compared against other established web document clustering algorithms, among them: Suffix Tree Clustering and Lingo. Comparison was executed on AMBIENT and MORESQUE datasets, using precision, recall, fmeasure, SSL k and other metrics. Results show a considerable improvement in clustering quality and performance.