This whitepaper explores how root cause analysis (RCA) in complex telecom networks can be enhanced using data mining techniques. Traditional RCA methods are often manual, slow, and inefficient when dealing with massive alarm datasets generated by fiber networks. The paper introduces an automated approach using pattern matching and a modified Smith-Waterman algorithm to analyze alarm sequences and identify recurring patterns. By incorporating temporal data and clustering similar alarm floods, the system can group issues and link them to known root causes. This significantly improves fault detection, reduces analysis time, and allows operators to focus on new or unknown problems. The approach ultimately increases network reliability, operational efficiency, and service continuity.

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