Your IP: 54.92.155.160 United States Near: United States

Lookup IP Information

2 3 4 5 6 7 8 Next

Below is the list of all allocated IP address in 166.207.0.0 - 166.207.255.255 network range, sorted by latency.

In mathematics, string metrics (also known as similarity metrics) are a class of textual based metrics resulting in a similarity or dissimilarity (distance) score between two text strings for approximate matching or comparison and in fuzzy string searching. For example the strings "Sam" and "Samuel" can be considered to be similar. A string metric provides a floating point number indicating an algorithm-specific indication of similarity. The most widely known string metric is a rudimentary one called the Levenshtein Distance (also known as Edit Distance). It operates between two input strings, returning a score equivalent to the number of substitutions and deletions needed in order to transform one input string into another. Simplistic string metrics such as Levenshtein distance have expanded to include phonetic, token, grammatical and character-based methods of statistical comparisons. A widespread example of a string metric is DNA sequence analysis and RNA analysis, which are performed by optimised string metrics to identify matching sequences. String metrics are used heavily in information integration and are currently used in fraud detection, fingerprint analysis, plagiarism detection, ontology merging, DNA analysis, RNA analysis, image analysis, evidence-based machine learning, database data deduplication, data mining, Web interfaces, e.g. Ajax-style suggestions as you type, data integration, semantic knowledge integration, etc.. List of string metrics Hamming distance Levenshtein distance and Damerau–Levenshtein distance Needleman–Wunsch distance or Sellers' algorithm Smith–Waterman distance FASTA BLAST Gotoh distance or Smith-Waterman-Gotoh distance Monge Elkan distance Block distance or L1 distance or City block distance Jaro–Winkler distance Soundex distance metric Matching coefficient Dice's coefficient Jaccard similarity or Jaccard coefficient or Tanimoto coefficient Tversky index Overlap coefficient Euclidean distance or L2 distance Cosine similarity Variational distance Hellinger distance or Bhattacharyya distance Information radius (Jensen–Shannon divergence) Harmonic mean Skew divergence Confusion probability Tau metric, an approximation of the Kullback–Leibler divergence Fellegi and Sunters metric (SFS) TFIDF or TF/IDF Maximal matches See also String matching SimMetrics - an implementation Carnegie Mellon University open source library External links http://www.dcs.shef.ac.uk/~sam/stringmetrics.html A fairly complete overview