The Role Of Data Mining For Information Extraction

Data Mining is an analytical process that primarily involves searching through vast amounts of data to spot useful, but initially undiscovered, patterns. The data mining process typically involves three major steps—exploration, model building and validation and finally, deployment.

As stated above, data mining is often used to solve business decision problems, “it provides ways to quantitatively measure what business users should already know qualitatively” (Linoff, 2004). A growing number of industries are using data mining to become more competitive in their market by primarily focusing on the customers; increasing their customer relationships and increasing customer acquisition.

Data Mining, a sub-branch of computer science, involving statistics, methods and calculations to find patterns in large amount of data sets, and database systems. Generally, data mining is the process to examine data from different aspects and summarizing it into meaningful information. Data mining techniques depict actions and future trends, allowing any individual to make better and knowledge-driven decisions.[1][2]

Data mining is when a financial analyst gathers consumer information and looks for patterns that a business can exploit. A simplified data mining example is when a restaurant manager knows the local yearly convention schedule based on experience. The manager can cross-reference that information with historical sales results to predict such things as forecasted profit or labor demand. With this information, the manager can estimate an advertising budget or hire temporary staff to handle anticipated work load. When medium to large-sized businesses use data mining, they uncovering these same information points; however, revenue gains can range from millions to billions of dollars. There are several techniques that firms frequently employ to find gold in information.

Companies and organizations all over the world are blasting on the scene with data mining and data warehousing trying to keep an extreme competitive leg up on the competition. Always trying to improve the competiveness and the improvement of the business process is a key factor in expanding and strategically maintaining a higher standard for the most cost effective means in any business in today’s market. Every day these facilities store large amounts of data to improve increased revenue, reduction of cost, customer behavior patterns, and the predictions of possible future trends; say for seasonal reasons. Data

The overall goal of the data mining process is to extract information from data sets and transform it into an understandable structure such as patterns and knowledge for further use [3].

Data Mining is the computerized acknowledgment of diverse patterns in extensive data sets that are past analysis. It utilizes diverse mathematic algorithms to locate the right information as well as foresee the probability of future events. Some key properties that I learned in this topic are:

Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data. There is a huge amount of data available in the Information Industry. This data is of no use until it is converted into useful information. It is necessary to analyse this huge amount of data and extract useful information from it. Extraction of information is not the only process we need to perform; data mining also involves other processes such as Data Cleaning, Data Integration, Data Transformation, Data Mining, Pattern Evaluation and Data Presentation [12].

A data stream is a real time, continuous, structured sequence of data items. Mining data stream is the process of extracting knowledge from continuous, rapid data records. Data arrives faster, so it is a very difficult task to mine that data. Stream mining algorithms typically need to be designed so that the algorithm works with one pass of the data. Data streams are a computational challenge to data mining problems because of the additional algorithmic constraints created by the large volume of data. In addition, the problem of temporal locality leads to a number of unique mining challenges in the data stream case. The data mining techniques namely clustering, classification and frequent pattern mining are applied to extract the knowledge

Data mining also called knowledge discovery in data, began in the early1980s, and has been fast growing in today’s various industries as an essential information technology. “Data mining is a step in the KDD process that consists of applying data analysis and discovery algorithms that, under acceptable computational efficiency limitations, produce a particular enumeration of patterns (or

Data Mining is the non-trivial extraction of potentially useful information about data. In other words, Data Mining extracts the knowledge or interesting information from large set of structured data that are from different sources. There are various research domains in data mining specifically text mining, web mining, image mining, sequence mining, process mining, graph mining, etc. Data mining applications are used in a range of areas such as it is used for financial data analysis, retail and telecommunication industries, banking, health care and medicine. In health care, the data mining is mainly used for disease prediction. In data mining, there are several techniques have been developed and used for predicting the diseases

Mining valuable patterns in different data streams have been a significant research area in data mining research during the last decade. There are several proposed techniques for data mining that have been developed for mining patterns from different text documents. But to determine the method in which the patterns are discovered effectively is a popular issue in data mining research including text mining area. Most of the popular methods in text mining make use of term-based methodology which involves problems like synonym and polysemy. Some research on text mining proves that the pattern based or phrase based approach performs better compared to the term-based approach but there is no concrete evidence to prove this point. The

data is also Growing. It has resulted large amount of data stock in databases , depot and other repositories . therefore the Data mining comes into model to explore and analyses the databases to extract the interesting and previously obscure patterns and rules well-known as association rule mining

Data mining is the process used to analyse large quantities of data and gather useful information from them. It extracts the hidden information from large heterogeneous databases in many different dimensions and finally summarizes it into categories and relations of data. Clustering and classifications are the two main techniques of data mining followed by association rules, predictions, estimations and regressions. Many fields imply on data mining like games, business, surveillance, science and engineering etc.