Activity-based correlation of personal documents and their visualization using association rule mining

It is a common observation nowadays that the personal information of user is difficult to manage, the material which is copied by the users to their personal system are often forgotten by the users. So when they require their information it becomes very difficult to find the relevant information from huge repository. We have introduced a method using which the activities of user for reading documents are captured from running process list and managed in a dataset along with accessing time, then frequent item set and associated weights are calculated for each document with other using Apriori Algorithm and confidence measure in conjunction with combined access time. When user searches a document, the document list appears using any conventional model of retrieval, we have used primary metadata including title, author, type for document searching. Beside this, a visual interface is designed to display the list correlated document on the basis of users activities may help them to indentify documents according to their past activities.