1. A data clustering method, for clustering a plurality of data points, wherein each of the data points has at least one feature value, and the data clustering method comprising: sorting the data points and generating a processing sequence based on a sorting result; using a non-iterative mechanism to cluster the data points into a plurality of data clusters according to the processing sequence; and optimizing the generated data clusters, wherein the step of using the non-iterative mechanism to cluster the data points into the data clusters according to the processing sequence comprises: sequentially selecting a target data point from the data points according to the processing sequence; determining whether any data group has been generated; when one or a plurality of current data groups has been generated, calculating one or a plurality of feature value differences between one or a plurality of feature values corresponding to the one or plurality of current data groups and the feature value of the target data point and identifying a minimum feature value difference among the one or plurality of feature value differences; determining whether the minimum feature value difference is smaller than a difference threshold of a data group corresponding to the minimum feature value difference among the one or plurality of current data groups; adding the target data point into the data group corresponding to the minimum feature value difference when the minimum feature value difference is smaller than the difference threshold of the data group corresponding to the minimum feature value difference; and updating the difference threshold of the data group added with the target data point according to the feature value of the target data point and a weight value.