IJCSI International Journal of Computer Science Issues, Vol. 3, 2009 ISSN (Online): 1694-0784 ISSN (Printed): 1694-0814 PR(i) = PageRank of page i PR( j) = PageRank of any other pages except page i. 

N j = Number of pages in the set Si jε = Inbound pages linking to page i

IJCSI
PageRank algorithm is mainly used for internet applications to find the rank of a page. The basis of the algorithm is that the rank of a page depends on the inbound links of the other pages. To apply this technique we need to compare links among the pages with the links among the features of the fault. But this study requires the ranking of features according to their importance. This makes PageRank algorithm inappropriate for this project. 

## 5. Similarity Matching

This chapter describes the similarity matching techniques for strings. Using these techniques, a concept is proposed to search similar faults when the symptoms of a fault are provided. Edit distance is a common term in matching algorithms. The word distance is used to compare different data for similarity. Edit distance is a measure to estimate differences between input elements. Different methods to calculate edit distance exist:

## Levenshtein Distance

Levenshtein distance is named after the Russian scientist Vladimir Levenshtein, who devised the algorithm in 1965. The Levenshtein distance between two strings is given by the minimum number of operations needed to transform one string into the other, where an operation is an insertion, deletion, or substitution of a single character. 

Levenshtein distance (LD) is a measure of the similarity between two inputs: the source s and the target input t. The distance is the number of deletions, insertions, or substitutions required to transform s into t. For example, If s is "math" and t is "math", then LD(s,t) = 0, because no transformations are needed. If s is "math" and t is "mats", then LD(s,t) = 1, because one substitution (change "h" to "s") is sufficient to transform s into t. The more different the inputs are, the greater the Levenshtein distance is. Insertion, deletion and substitution are the main criteria for determining Levenshtein Distance. The position of a character plays an important role to determine the distance. In this study, the description of a fault is dealt with. If Levenshtein Distance is applied to find out the similarity of faults it would not give a meaningful result as the positions of the strings should not have importance. That is why this technique will not be used in this study. 

## Damerau-Levenshtein Distance

Damerau-Levenshtein distance comes from Levenshtein distance that counts transposition as a single edit operation. The Damerau-Levenshtein distance is equal to the minimal number of insertions, deletions, substitutions and transpositions needed to transform one string into the other. Kukich [5] described several edit distance algorithms which use Damerau-Levenshtein distance. It has been proved that the use of Damerau-Levenshtein metric to calculate the similarity between two words is a slow process. For this reason this method is not well-suited for similarity matching in this project. 

## Needleman - Wunsch Distance

The Levenshtein distance algorithm assumes that the cost of all insertions, deletions, substitutions or conversions is equal. However, in some scenarios this may not be desirable and may mask the acceptable distances between inputs. Needleman-Wunsch has modified Levenshtein distance algorithm to add cost matrix as an extra input. This matrix structure contains two cost matrics for each pair of characters to convert from and to. The cost of inserting this character and converting between characters is listed in this matrix. 

This approach is not appropriate for use in this study's similarity matching for the same reason stated in Levenshtein approach.

## Hamming Distance

The Hamming distance [6] H is defined for the same length inputs. For two inputs s and t, H(s, t) is the number of places in which the two strings differ, i.e., have different characters. 

Hamming Distance is used in information theory. This method can not be applied in similarity matching for automotive faults since Hamming Distance only considers the differences among the two inputs. 

## Weighted Edit Distance
