Due to the current encroachments in technology and also sharp lessening of storage cost, huge extents of documents are being put away in repositories for future references. At the same time, it is time consuming as well as costly to recover the user intrigued documents, out of these gigantic accumulations. Searching of documents can be made more efficient and effective if documents are clustered on the premise of their contents. This article uncovers a comprehensive discussion on various clustering algorithm used in text mining alongside their merits, demerits and comparisons. Further, author has likewise examined the key challenges of clustering algorithms being used for effective clustering of documents.

Due to the availability of various image processing tools forgery over an image can be performed very easily but very difficult to identify. In copy-move forgery, a segment is copied from the original image and pasted at some other location on the same image to hide significant objects of image or to bring additional information which is originally not present in image. Nowadays, this forgery technique is drawing researcher's attention. Till now many solutions are presented by researchers to detect such type of forgery in images. Several post-processing operations like rotation, alteration in intensity, noise addition, filtering and blurring can be applied over copy-moved segment which makes detection of forgery very difficult. Copy-move forgery detection is mainly based on finding similarity present in an image and establish a relationship between genuine image parts and pasted portion of the image. This paper is centralized towards providing survey to forgery detection techniques based on different block-based methods. In block-based methods image is divided in blocks of fixed dimension and further features are extracted corresponding to each block of image. Forged blocks are identified utilizing the similarity present between feature vectors.