IoT data mining is an important link in IoT technology. Traditional data mining can no longer meet the requirements of IoT technology, and the emergence of cloud computing, with its powerful computing power and ability to store large-scale data, has provided a new direction for IoT data mining. In such a context, a cloud based IoT data mining technology has emerged, which has attracted wide attention worldwide. In this paper, we study the K-means algorithm in data mining to solve the problems of K-means algorithm such as unstable clustering and inability to handle large-scale IoT data. This paper mainly. The experiments show that the clustering effect of ALCDK-means algorithm is significantly improved, and the accuracy rate is greatly improved compared with K-means algorithm. Finally, the shortcomings of ALCDK-means algorithm, such as the inability to effectively handle large-scale data sets, are addressed by parallelizing the algorithm based on the Hadoop platform to solve the problem that traditional data mining cannot handle large-scale data. The experimental results show that the parallelization of ALCDK-means algorithm solves the problems of traditional data mining such as long time consuming and inefficient to a certain extent, which in turn is beneficial for large-scale data mining.
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