As a widely used automated software testing technique, the primary goal of fuzzy testing is to explore as many code areas of the program under test as possible, thereby achieving higher coverage as well as detecting more bugs or errors. Most of existing fuzzy testing methods schedule the seed based on the historical mutation data of the seed, which is simpler to implement but ignores the distribution of program space explored by the seed, resulting in that the testing may fall into only a single region of the program to be probed, and causing the waste of testing resources. This study proposes the Cluzz, a fuzzing approach of clustering analysis-driven in seed scheduling. Firstly, Cluzz analyzes the difference between seeds in the feature space by combining the distribution of seed execution path coverage, and uses cluster analysis to classify the distribution of seeds execution in the program space. And then, Cluzz prioritizes the seeds according to the path coverage patterns of different seed clusters and the results of cluster analysis, explores the rare code regions and prioritizes the seeds with higher evaluation scores. Secondly, energy is allocated to the seeds by their evaluation scores, and the interesting inputs obtained from mutations are retained and categorized to update the seed cluster information. Cluzz reevaluates the seeds based on the updated seed clusters to ensure the validity of seeds during testing process, thereby exploring more unknown code regions in a limited time and improving the coverage of the program under test. Finally, the Cluzz is implemented on three current mainstream fuzzers and extensive testing work is conducted on eight popular real-world programs. The results show that Cluzz can detect an average of 1.7 times more unique crashes than a regular fuzzer, and it also outperforms a benchmark fuzzer by an average of 22.15% in terms of the number of new edges found. In addition, compared with the existing seed scheduling methods, the comprehensive performance of Cluzz is better than that of other benchmark fuzzers.