# Incident (Ticket) Clustering Major issue and problem detection are critical aspects of maintaining and improving IT service quality. Additionally, identifying and addressing the root causes of problems, organizations can reduce the occurrence of incidents, enhance service reliability, and improve user satisfaction. Incident and event clustering (generally referred to in Aisera platform as Ticket Clustering) plays a major role to operationalize these use cases. Finally Ticket Clustering also helps with the following use cases: 1. **Identifying Common Topics in Support Tickets** : Customers can identify clusters of common topics in support tickets. The tool can analyze ticket titles, descriptions, and comments and other case attributes to group related issues, providing a clear understanding of prevalent issues. 2. **Historical Case Analysis** Customers can provide Aisera with a few tickets associated with a common theme, and the platform will find all historical cases related to that theme. This capability allows teams to gather metrics and insights to present to product teams, justifying the need for prioritizing solutions to recurring issues. Ticket clustering leverages Aisera's Large Language Models (LLMs) and sophisticated techniques based on temporal and other ticket attributes to automatically group related support tickets. LLMs analyze the textual content of tickets, such as titles, descriptions, and comments, to identify underlying themes and similarities. These models understand the context and nuances of language, allowing them to accurately cluster tickets that pertain to similar issues. Additionally, the platform considers temporal attributes and other relevant criteria—such as ticket status, categories, and subcategories—using time-based algorithms to enhance the clustering accuracy. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.aisera.com/ai-ops/incident-ticket-clustering.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.