Information Retrieval Systems (IRS) use phrases to retrieve, organize, describe and index documents. The phrases identified, normally predict the presence of other phrases in those documents. Documents are then indexed, in accordance to the existing phrases. The index is partitioned into multiple ones, including a primary index and secondary. The primary one stores phrases with relevant rank ordered documents. The secondary one stores excess documents in document order.



One application of this concept is meaning-based search. This allows web users to locate information that is close in meaning to concepts being searched. Searching is done by determining a semantic distance between the first and second meaning differentiator, since this distance represents their closeness in meaning. Results for search queries are presented where the target data elements closest in meaning, based on their determined semantic distance, are ranked higher.

Data Mining is also called knowledge Discovery and Data Mining (KDD). Data mining is the extraction of useful patterns and relationships from data sources. It uses the statistical and pattern matching techniques. Also, Data Mining includes data from statistics, machine learning, databases, data visualization and other fields.

Data Mining is often overlooked, when in fact, it can provide us very interesting information that statistical methods are unable to produce. The data SEOs have is often vast, and noisy, meaning that it is imprecise and data structure is complex. The issues that appear in Data Mining are noisy data, missing values, static data, sparse data, dynamic data, relevance, interestingness, heterogeneity, algorithm efficiency, size and complexity of data. These types of problems often occur in large amounts of data, like in search engine indexes.