Abstract: Tiger has become the reserve animal. Conservation of tiger has been the challenging task. This research would add a small account to the herculean task of conserving the species. This research proposes an algorithm from which the age of the tiger can be inferred. This research combines the domain of image processing with data mining to infer the age of tiger. Image processing techniques like image enhancement and segmentation plays a vital role in mining the image of the tiger. The image processing is complemented with data mining to find the age of tiger where data mining plays the role of analyzing the statistical report of confirming the age of the tiger. Several scientific researchers have carried out their research on the tiger reserve conservation. This research work proposes a method to find the age of the tiger, using color as a parameter. Color pixel based image classification and clustering techniques has been used to identify the age of the tiger. Clustering is a part which considers the principal of systematic techniques in handling. Clustering is the process of making a group of abstract objects into classes of similar objects. Image segmentation is the classification of an image into different groups. Many researches have been done in the area of image segmentation using clustering. There are different methods and one of the most popular methods is k-means clustering algorithm. In working on k-mean clustering approach to cluster the data. Several strategies have been proposed for enhancing the performance of k-means clustering algorithm. DBSCAN is designed to discover clusters of arbitrary shape. DBSCAN which exploits its characteristics and at the same time improves its limitation, so, it is used widely in the clustering technique. The Mountain Clustering (FMC) method is a relatively simple and effective approach to approximate estimation of cluster centers on the basis of a density measure. ISODATA algorithm (Iterative Self-Organizing Data Analysis Technique Algorithm) which allows the number of clusters to be adjusted automatically during the iteration by merging similar clusters and splitting clusters with large standard deviations. The Modified k-Means Clustering (MKMC) and Fuzzy ISODATA (FISODATA), FBDBSCAN, FBMC cluster for making the algorithms much less time consuming, greater high-quality and efficient for higher clustering accuracy rate with reduction is time complexity.