The methods of high-dimensional clustering have been applied for variety of problems and in case of decisive support systems, there are few approaches discussed earlier, but suffers with the problem of false indexing ratio with poor clustering accuracy and higher time complexity. To overcome the issue of poor clustering accuracy, a novel Kⁿ Fast Clustering algorithm is discussed in this paper. The method generates rule sets using the data records from the data set. First the dimension N is identified and for each dimension the range values are identified. From identified fuzzy values, the method computes disease impact factor for each of the dimension or symptoms towards each disease class. Based on the impact factor and the data points, we generate rule sets that consist of a single rule for each of the disease class. The Kⁿ Fast clustering algorithm uses the fuzzy rule sets generated and for each data point from the data set, the clustering algorithm computes K^N dimensional similarity measure. Based on computed similarity measure, the data points are assigned a class, and the method reduces the false indexing, overlapping, and time complexity of clustering.