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US 20050165825A1 (19) United States (12) Patent Application Publication (10) Pub. N0.: US 2005/0165825 A1 (43) Pub. Date: Turks et al. (54) AUTOMATIC QUERY CLUSTERING (52) Jul. 28, 2005 Us. 01. ............................................................

How to fill out automatic query clustering

1. To fill out automatic query clustering, first understand what automatic query clustering is. It is a technique used in information retrieval to group similar user queries together for better organization and analysis.
2. Begin by collecting a large dataset of query logs or search queries from users. This dataset will serve as the input for the automatic query clustering algorithm.
3. Preprocess the query data by removing stop words (common words that do not add much meaning to the queries) and applying stemming or lemmatization techniques to normalize words and reduce them to their base form.
4. Choose a suitable algorithm for automatic query clustering. Some popular algorithms for this purpose include K-means clustering, hierarchical clustering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise).
5. Implement the chosen clustering algorithm and apply it to the preprocessed query data. The algorithm will group similar queries together based on various similarity measures, such as cosine similarity or Jaccard similarity.
6. Evaluate the performance of the automatic query clustering algorithm by measuring metrics like clustering purity or silhouette score. This evaluation will help determine the effectiveness of the clustering results and identify any areas for improvement.
7. Once the automatic query clustering process is complete, analyze the clustered query groups to gain insights and improve information retrieval systems. Grouped queries can provide valuable information on user intent, popular topics, or emerging trends.

Now, let's discuss who needs automatic query clustering:

1. Search engines: Automatic query clustering is beneficial for search engines like Google, Bing, or Yahoo. By clustering similar queries, search engines can improve their understanding of user intent and provide more accurate search results.
2. E-commerce websites: Online retailers can benefit from automatic query clustering to gain insights into customer preferences, popular product categories, or emerging trends. Clustered queries can help optimize search functionality and improve product recommendations.
3. Customer support teams: Automatic query clustering can be used by customer support teams to categorize and analyze customer queries. This can help identify common issues, improve response times, and provide personalized support to customers.
4. Research institutions: Research institutions can use automatic query clustering to analyze large volumes of research queries and identify patterns or emerging research areas. This can aid in organizing research data and identifying collaboration opportunities.

In conclusion, automatic query clustering is a valuable technique for organizing and analyzing user queries. It can benefit various entities like search engines, e-commerce websites, customer support teams, and research institutions by improving information retrieval and providing valuable insights.

For pdfFiller’s FAQs

What is automatic query clustering?

Automatic query clustering is a process of grouping similar queries together based on certain criteria for efficient processing and analysis.

Who is required to file automatic query clustering?

Organizations or individuals who handle a large volume of queries and need to streamline the process.

How to fill out automatic query clustering?

Automatic query clustering can be filled out by using software or tools that can group queries based on predefined rules or parameters.

What is the purpose of automatic query clustering?

The purpose of automatic query clustering is to organize and categorize queries to make it easier for analysis, reporting, and decision-making.

What information must be reported on automatic query clustering?

The reported information on automatic query clustering may include the number of queries, categories of queries, time taken to process each category, and any related metrics.

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