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The TF – IDF stands for Term Frequency – Inverse Document Frequency, using this TF-IDF weight of the document is calculated. It is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. It is often used as a weighting factor in searches of information retrieval, text mining, and user modeling. The TF-IDF weight is calculated by two terms:
The (TF) which measures how frequently a term occurs in a document. Since every document is different in length, it is possible that a term would appear much more times in long documents than shorter ones. Thus, the term frequency is often divided by the document length as a way of normalization: Suppose we have a set of English text documents and wish to rank which document is most relevant to the query, “the brown cow”. A simple way to start out is by eliminating documents that do not contain all three words “the”, “brown”, and “cow”, but this still leaves many documents. To further distinguish them, we might count the number of times each term occurs in each document; the number of times a term occurs in a document is called its term frequency. However, in the case where the length of documents varies greatly, adjustments are often made (see definition below).
The first form of term weighting is due to Hans Peter Luhn (1957) which may be summarized as: The weight of a term that occurs in a document is simply proportional to the term frequency. TF (t) = (Number of times term t appears in a document) / (Total number of terms in the document).
The (IDF) which measures how important a term is. While computing TF, all terms are considered equally important. However it is known that certain terms, such as “is”, “of”, and “that”, may appear a lot of times but have little importance. Thus we need to weigh down the frequent terms while scale up the rare ones, by computing the following: Because the term “the” is so common, term frequency will tend to incorrectly emphasize documents which happen to use the word “the” more frequently, without giving enough weight to the more meaningful terms “brown” and “cow”. The term “the” is not a good keyword to distinguish relevant and non-relevant documents and terms, unlike the less-common words “brown” and “cow”. Hence an inverse document frequency factor is incorporated which diminishes the weight of terms that occur very frequently in the document set and increases the weight of terms that occur rarely. Karen Spärck Jones (1972) conceived a statistical interpretation of term specificity called Inverse Document Frequency (IDF), which became a cornerstone of term weighting:
The specificity of a term can be quantified as an inverse function of the number of documents in which it occurs. IDF (t) = log_e (Total number of documents / Number of documents with term t in it).
WHOIS: WHOIS is a query and response protocol that is widely used for querying databases that store the registered users or assignees of an Internet resource, such as a domain name, an IP address block, or an autonomous system, but is also used for a wider range of other information. The protocol stores and delivers database content in a human-readable format.
Locating Phishing Server:
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