  Clustering is an extensive research area in data science. The aim of
clustering is to discover groups and to identify interesting patterns in
datasets. Crisp (hard) clustering considers that each data point belongs to one
and only one cluster. However, it is inadequate as some data points may belong
to several clusters, as is the case in text categorization. Thus, we need more
flexible clustering. Fuzzy clustering methods, where each data point can belong
to several clusters, are an interesting alternative. Yet, seeding iterative
fuzzy algorithms to achieve high quality clustering is an issue. In this paper,
we propose a new linear and efficient initialization algorithm MaxMin Linear to
deal with this problem. Then, we validate our theoretical results through
extensive experiments on a variety of numerical real-world and artificial
datasets. We also test several validity indices, including a new validity index
that we propose, Transformed Standardized Fuzzy Difference (TSFD).
