Attribute　clustering　has　teen　previously　employed　to　detect　statistical　dependence　between　subsets　of　variables.　We　propose　a　novel　attribute　clustering　algorithm　motivated　by　research　of　complex　networks,　called　the　Star　Discovery　algorithm.　The　algorithm　partitions　and　indirectly　discards　inconsistent　edges　from　a　maximum　spanning　tree　by　starting　appropriate　initial　modes;　therefore　generating　stable　clusters.　It　discovers　sound　clusters　through　simple　graph　operations　and　achieves　significant　computational　savings.　We　compare　the　Star　Discovery　algorithm　against　earlier　attribute　clustering　algorithms　and　evaluate　the　performance　in　several　domains.