Delay Tolerant Network (DTN) is evolved from Mobile Adhoc Network, MaNet. It is sparse and intermittently connected mobile ad hoc network where reliable communication and end-to-end connectivity is not available for message transmission. The end to end connectivity is not ensured in Delay Tolerant Network. Only high latency applications are used in DTN. Latency may be in hours or days. The store and forward approach helps to increase message delivery probability in DTN irrespective of time taken to delivered message over normal MaNet. In DTN, the two or more nodes can exchange message when they move in transmission range of each other. Routing issue is very important due to limited resources for message storage and forwarding. This Routing issue is considered by many researchers which results in many routing protocol based on Flooding and Forwarding Approach. In this paper we try to summarize the routing protocols till date which are available in literature. We also try to compare the entire routing protocol. This paper gives right direction to researchers to proceed for designing a new routing protocol for Delay Tolerant Network.

Now a day, the text document is spontaneously increasing over the internet, e-mail and web pages and they are stored in the electronic database format. To arrange and browse the document it becomes difficult. To overcome such problem the document preprocessing, term selection, attribute reduction and maintaining the relationship between the important terms using background knowledge, WordNet, becomes an important parameters in data mining. In these paper the different stages are formed, firstly the document preprocessing is done by removing stop words, stemming is performed using porter stemmer algorithm, word net thesaurus is applied for maintaining relationship between the important terms, global unique words, and frequent word sets get generated, Secondly, data matrix is formed, and thirdly terms are extracted from the documents by using term selection approaches tf-idf, tf-df, and tf2 based on their minimum threshold value. Further each and every document terms gets preprocessed, where the frequency of each term within the document is counted for representation. The purpose of this approach is to reduce the attributes and find the effective term selection method using WordNet for better clustering accuracy. Experiments are evaluated on Reuters Transcription Subsets, wheat, trade, money grain, and ship.

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