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This article proposes a simple frequent pattern mining algorithm using link structure. The “LinkRuleMiner” has a distinct feature that it has a very limited and precisely predictable main memory cost and runs very quickly in memory based settings. Moreover, it can be scaled up to very large databases using database partitioning. This article analyzes the coloring process of dyeing unit using newly proposed association rule mining algorithm “LinkRuleMiner” using frequent patterns. These frequent patterns have a confidence for different treatments of the dyeing process. These confidences help the dyeing unit expert called dyer to predict better combination or association of treatments. This article also proposes to implement LRM algorithm to the dyeing process of dyeing unit, which may have a major impact on the coloring process of dyeing industry to process their colors effectively without any dyeing problems, such as pales, dark spots on the colored yarn. This article shows that LinkRuleMiner has an excellent performance for various kinds of data to create frequent patterns, outperforms currently available algorithms in dyeing processing systems, and is highly scalable to mining large databases. It is a revised algorithm of HMine that does not need any adjustment of links. The revised algorithm has comparable performance with the original version and can be easily extended to use in parallel environment. Hence this article mainly contributes more on knowledge discovery of various shades of the color in the dyeing process.