With the exponential growth of data from various social networks like Facebook, Twitter, Mobile applications, Digital cameras, Sensor networks etc., and also from biomedical researches the overall data volume has increased tremendously. So analyzing and extracting fruitful information from such a dynamic data is very much challenging task today. Data grouping or clustering plays a vital role in handling big data which is the basic foot step in data mining, pattern recognition and also in medical predictions. The clustering techniques are very much suitable for handling big data in this case the learning parameters are computed from learning data. Clustering approaches can be classified into two categories namely- Hard clustering and soft clustering. In hard clustering data is divided into clusters in such a way that each data item belongs to a single cluster only while soft clustering also known as fuzzy clustering forms clusters such that data elements can belong to more than one cluster based on their membership levels which indicate the degree to which the data elements belong to the different clusters. This paper deals with an attempt at studying the data clustering algorithms based on fuzzy techniques. These fuzzy clustering algorithms have been widely studied and applied in a variety of substantive areas