The utility lies in the fact that the wavelet transformed data can be limited. A compressed approximation of the information can be retained by saving only a small fraction of the principal of the wavelet coefficients. For instance, all wavelet coefficients higher than some user-defined threshold can be maintained. Some other coefficients are set to 0.The resulting data description is very sparse so that services that can take benefit of data sparsity are computationally very quick if implemented in wavelet space. The method also works to eliminate noise without smoothing out the main characteristics of the data, creating it efficient ... Read More

Attribute subset selection reduces the data set size by removing irrelevant or redundant attributes (or dimensions). The objective of attribute subset selection is to discover a minimum set of attributes such that the subsequent probability distribution of the data classes is as close as feasible to the original distribution obtained using all attributes.For n attributes, there are 2n possible subsets. An exhaustive search for the optimal subset of attributes can be extremely costly, specifically as n and the number of data classes raise. Hence, heuristic approaches that explore a reduced search space are generally used for attribute subset selection.These approaches ... Read More

Trend analysis defines the techniques for extracting a model of behavior in a time series that can be slightly or entirely hidden by noise. The methods of trend analysis have been generally used in detecting outbreaks and unexpected increases or decreases in disease appearances, monitoring the trends of diseases, evaluating the effectiveness of disease control programs and policies, and assessing the success of health care programs and policies, etc.Various techniques can be used to detect trends in item series. Smoothing is an approach that is used to remove the non-systematic behaviors found in time series. Smoothing usually takes the form ... Read More

Temporal data mining defines the process of extraction of non-trivial, implicit, and potentially essential data from large sets of temporal data. Temporal data are a series of primary data types, generally numerical values, and it deals with gathering beneficial knowledge from temporal data.The objective of temporal data mining is to find temporal patterns, unexpected trends, or several hidden relations in the higher sequential data, which is composed of a sequence of nominal symbols from the alphabet referred to as a temporal sequence and a sequence of continuous real-valued components called a time series, by utilizing a set of approaches from ... Read More

Cluster analysis is a branch of statistics that has been studied widely for several years. The benefit of using this technique is that interesting structures or clusters can be discovered directly from the data without utilizing any background knowledge, such as concept hierarchy.Clustering algorithms used in statistics, like PAM or CLARA, are reported to be inefficient from the computational complexity point of view. As per the efficiency concern, a new algorithm called CLARANS (Clustering Large Applications based upon Randomized Search) was developed for cluster analysis.PAM (Partitioning around Medoids) − It is assuming that there are n objects, PAM finds k ... Read More

Spatial data mining is the application of data mining to spatial models. In spatial data mining, analysts use geographical or spatial data to make business intelligence or different results. This needed specific methods and resources to get the geographical data into relevant and beneficial formats.There are several challenges involved in spatial data mining include recognizing patterns or discovering objects that are relevant to the questions that drive the research project. Analysts can be viewed in a large database area or other completely huge data set to discover only the relevant data, utilizing GIS/GPS tools or similar systems.The primitives of spatial ... Read More

Web mining defines the process of using data mining techniques to extract beneficial patterns trends and data generally with the help of the web by dealing with it from web-based records and services, server logs, and hyperlinks. Web mining aims to discover the designs in web information by grouping and analyzing data to receive important insights.Web mining can widely be viewed as the application of adapted data mining methods to the web, whereas data mining is represented as the application of the algorithm to find patterns on mostly structured data fixed into a knowledge discovery process.There are various applications of ... Read More

PageRank is a method for rating Web pages objectively and mechanically, paying attention to human interest. Web search engines have to organize with inexperienced clients and pages manipulating conventional ranking services. Some evaluation methods which count replicable natures of Web pages are unimmunized to manipulation.The task is to take advantage of the hyperlink structure of the Web to produce a global importance ranking of every Web page. This ranking is called PageRank.The mechanism of the Web depends on a graph with about 150 million nodes (Web pages) and 1.7 billion edges (hyperlinks). If Web pages A and B link to ... Read More

Web mining is the application of machine learning (data mining) approaches to web-based data for the goals of learning or deriving knowledge. Web mining methodologies can be defined into one of three distinct elements which are as follows −Web Usage Mining − Web usage mining is a kind of web mining that enables the set of Web access data for Web pages. This usage data supports the direction leading to accessed Web pages.This data is gathered automatically into connection logs via the Web server. CGI scripts provide useful data including referrer logs, user subscription data, and survey logs. This category ... Read More

Web content mining is referred to as text mining. Content mining is the browsing and mining of text, images, and graphs of a Web page to decide the relevance of the content to the search query.This browsing is done after the clustering of web pages through structure mining and supports the results depending upon the method of relevance to the suggested query.With a large amount of data that is available on the World Wide Web, content mining supports the results lists to search engines in order of largest applicability to the keywords in the query.It can be defined as the ... Read More