Decisions in real-life can be adversely affected by various uncertainty-sources such as perception-diversity, data-structure and analytical tools. Fuzzy clustering can successfully handle the uncertainties while recognizing patterns in any given data. Nevertheless, type-1 fuzzy clustering techniques has uncertainties on account of precise-nature of primary memberships. Type-2 fuzzy clustering are preferred by many researchers to manage uncertainty in its type-1 version. In type-2 fuzzy clustering, order of fuzziness (fuzzifier) is obtained by interval-valued or general type-2 fuzzy sets. Interval type-2 fuzzy clustering can reduce the computational complexity of type-2 fuzzy set mathematics. However, general type-2 fuzzy clustering scrutinizes uncertainty in fuzzifier using linguistic sets. Interval and general type-2 fuzzy clustering algorithms include type-reduction approaches to obtain type-1 fuzzy sets. Besides, full type-2 fuzzy c-means can be used as a foundation approach in type-2 fuzzy inferences. Although this algorithm includes precise-fuzzifier, it gives a point of view to practically calculate secondary memberships. In this paper, an adaptive type-2 fuzzy clustering algorithm is proposed to manage the uncertainty-sources with a self-reduction procedure. Several numerical results and comparisons are given to demonstrate the achievement of this proposed algorithm. The performance of the proposed algorithm is compared with type-1 and type-2 versions for various multi-dimensional pattern sets from UCI-patterns, Berkeley segmentation database and a real-life application related to sustainable supplier selection in an automotive industry. Consequently, the proposed algorithm reveals fast, convenient and precise results.

现实生活中的决策可能会受到各种不确定性来源的不利影响，例如感知多样性、数据结构和分析工具。模糊聚类可以成功地处理不确定性，同时识别任何给定数据中的模式。然而，由于主要成员的精确性，type-1 模糊聚类技术具有不确定性。许多研究人员首选 2 型模糊聚类来管理其 1 型版本中的不确定性。在 2 型模糊聚类中，模糊度（模糊器）的阶数是通过区间值或一般 2 型模糊集获得的。区间二类模糊聚类可以降低二类模糊集数学的计算复杂度。然而，一般的 2 型模糊聚类使用语言集仔细检查模糊器中的不确定性。区间和一般 2 型模糊聚类算法包括类型约简方法以获得 1 型模糊集。此外，完整的 2 型模糊 c 均值可以用作 2 型模糊推理的基础方法。尽管该算法包括精确模糊器，但它提供了一种实际计算二级成员资格的观点。在本文中，提出了一种自适应的 2 型模糊聚类算法，通过自减少程序来管理不确定性源。给出了几个数值结果和比较来证明该算法的实现。对于来自 UCI 模式的各种多维模式集，将所提出的算法的性能与类型 1 和类型 2 版本进行比较，伯克利细分数据库和与汽车行业可持续供应商选择相关的现实应用。因此，所提出的算法揭示了快速、方便和精确的结果。