Power　outage　analysis　is　critical　for　ensuring　grid　reliability,　optimizing　infrastructure,　and　improving　emergency　response　strategies.　Traditional　methods　for　analyzing　power　outage　patterns　often　face　inefficiencies　due　to　the　large-scale,　real-time　nature　of　outage　data　and　the　complex　dependencies　between　events.　This　research　introduces　a　data　mining　framework　leveraging　the　Apriori　algorithm　within　Association　Rule　Mining　to　extract　meaningful　patterns　from　real-time　power　outage　data.　The　proposed　model　utilizes　the　ODIN　Real-time　Outages　dataset,　which　provides　near　real-time　power　outage　information　at　the　county　level　across　the　China.　By　applying　the　Apriori　algorithm,　the　framework　efficiently　identifies　frequent　outage　patterns,　uncovering　hidden　relationships　between　occurrences,　affected　regions,　and　influencing　factors.　Experimental　results　validate　the　effectiveness　of　the　approach,　achieving　a　Rule　Confidence　of　92.4%　and　a　Support　Threshold　of　85.7%,　ensuring　high-quality　pattern　extraction.　Additionally,　a　Lift　Ratio　of　1.8　and　Association　Rule　Coverage　of　89.6%　confirm　the　model＇s　robustness　and　predictive　capability.　These　findings　highlight　the　potential　of　Apriori-based　rule　mining　in　transforming　outage　management　by　providing　insights,　enhancing　predictive　maintenance,　and　supporting　intelligent　decision-making.　©　2025　IEEE.