IJCSI International Journal of Computer Science Issues, Vol. 3, 2009 ISSN (Online): 1694-0784 ISSN (Printed): 1694-0814 provided fault symptoms. The similarities of fault symptom radio hu are 100% (fault id 49) with the database fault radio hu and 68% (fault id 40) with the database fault Preconditions: radio hu message. The result of this fault matching is shown in figure 3. 

![0_image_1.png](0_image_1.png)

The similarities of fault symptom radio hu message are 100% (id 40) with database fault radio hu and 84% (id 41) with database fault radio: radio. message; audio hu radio message message and 84% (id 45) with database fault radio hu message message messaeg. The result of this fault matching is shown in figure 4. 

![0_image_3.png](0_image_3.png)

![0_image_0.png](0_image_0.png)

The similarities of fault symptom radio dvd message are 61% (id 41) with the database fault radio: radio. message; audio hu radio message message and 56% (id 45) with the database fault radio hu message message message. The result of this fault matching is shown in figure 5. The similarities of fault symptom radio dvd are 58% with database faults radio (id 50) and radio radio(id 51) and radio radio radio (id 52). The result of this fault matching is shown in figure 6. 

![0_image_2.png](0_image_2.png)

Based on the above result analysis it can be concluded that the similarity of a user provided fault is higher if the symptom of the fault matches more closely with any database fault. It satisfies the requirement of finding similar faults for a fault symptom. 

## 7. Conclusion

In this paper feature selection, pattern recognition, page ranking algorithms have been discussed to process input data and system database. Different similarity matching algorithms have also been explained. Cosine similarity 