Data Mining Techniques And Their Applications

Data Mining. It is the process of discovering interesting knowledge that are gathered and significant structures from large amounts of data stored in data warehouse or other information storage.

Data mining is another concept closely associated with large databases such as clinical data repositories and data warehouses. However data mining like several other IT concepts means different things to different people. Health care application vendors may use the term data mining when referring to the user interface of the data warehouse or data repository. They may refer to the ability to drill down into data as data mining for example. However more precisely used data mining refers to a sophisticated analysis tool that automatically dis­ covers patterns among data in a data store. Data mining is an advanced form of decision support. Unlike passive query tools the data mining analysis tool does not require the user to pose individual specific questions to the database. Instead this tool is programmed to look for and extract patterns, trends and rules. True data mining is currently used in the business community for market­ ing and predictive analysis (Stair & Reynolds, 2012). This analytical data mining is however not currently widespread in the health care community.

The author points out that although there are existing algorithms and tools available to handle Big Data, they are not sufficient as the volume of data is exponentially increasing every day. To show the usefulness of Big Data mining, the author highlighted the work done by United Nations. In order to further enhance the reader’s perspective, the author provided research work of various professionals to educate its readers about the most recent updates in Big Data mining field. The author further describes the controversies surrounding Big Data. The author has first provided the context and exigence by elaborating on why we need new algorithm and tools to explore the Big Data. The author used the strategy of highlighting the logos by mentioning the research work of different industry professionals, workshops conducted on Big Data and was able to appeal to connect to the reader’s ethos. The author also used pathos by urging the budding Big Data researchers to further dig deep into the topic and explore this area

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.

Data mining: is a process of discovering patterns in large data involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems with the aim of extracting information and transforming it into an understandable structure for further use.

Data mining is really just the next step in the process of analyzing data. Instead of getting queries on standard or user-specified relationships, data mining goes a step farther by finding meaningful relationships in data. Relationships that were thought to have not existed, or ones that give a more insightful view of the

Data, Data everywhere. It is a precious thing that will last longer than the systems. In this challenging world, there is a high demand to work efficiently without risk of losing any tiny information which might be very important in future. Hence there is need to create large volumes of data which needs to be stored and explored for future analysis. I am always fascinated to know how this large amount of data is handled, stored in databases and manipulated to extract useful information. A raw data is like an unpolished diamond, its value is known only after it is polished. Similarly, the value of data is understood only after a proper meaning is brought out of it, this is known as Data Mining.

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:

Big Data refers to the large amounts of data that businesses deal with on a day-to-day basis. Big Data can be defined in many ways, but this is one of the most general definitions. One of the primary uses businesses have for Big Data is to provide assistance for decision making. In order for Big Data to be effective, organizations must utilize the data to their advantage. The quality of information is what matters, not the quantity. Organizations may have virtually unlimited amounts of data to work with, but they must locate the helpful portions of data in order for it to be effective and serve its purpose. In addition, some data may not be helpful at the present moment in time, but will be needed for analysis in the future (“What Is Big Data?”,

Data mining enables users to discover hidden patterns without a predetermined idea or hypothesis about what the pattern may be. The data mining process can be divided into two categories: discovering patterns and associations, and predicting future trends and behaviors using the patterns. The power of data mining is evident as it can bring forward patterns that are not even considered by the user to search for. Hence, you have the answer to a question that was never asked. This is especially helpful when you are dealing with a large database where there may be an infinite number of patterns to identify. Interesting to note is the fact that “the more data in the warehouse, the more patterns there are, and the more data we analyze the fewer patterns we find.” What this means is that when there is richness of data and data patterns, it may be best to data mine different data segments separately, so that the influence of one pattern does not dilute the effect of another pattern in a large database.

Data mining is the process through which previously unknown patterns in data were discovered. Another definition would be “a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and subsequent knowledge from large databases.” This includes most types of automated data analysis. A third definition: Data mining is the process of finding mathematical patterns from (usually) large sets of data; these can be rules, affinities, correlations, trends, or prediction models.

Data Mining is generally used for four main tasks: (1) to improve the process of making new customers and retaining customers; (2) to reduce fraud; (3) to identify internal wastefulness and deal with that wastefulness in operations, and (4) to chart unexplored areas of the internet

Data mining is often referenced to text mining. This concept is constructed of researching information, storing information, and presents information. This concept is mainly about the searching of patterns new or old. Along with discovering patterns it is about predicting them as well. Text Mining can be referred as text data mining. This concept involves controlled patterns of data involvement. This is a concept focused on the basic analytical techniques. This term has many different name, but is still defined as using a wide variety of countless data bases to retrieve information. The retrievals no matter where they are collected from are valuable to a business as long as they are from a reliable truthful source.

Huge amounts of data are collected nowadays from different application domains and are not feasible to analyze all these data manually. Knowledge Discovery in Databases (KDD) is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. In recent years, soft computing became more and more attractive for the researchers, who work in the related research field of data mining. This paper concerns primarily about how to use soft computing model to extract knowledge from data mining (database). The data mining preprocessing techniques have been applied on the available data to clean it in proper form to extract the knowledge from the data. Thereafter, statistical analysis and soft computing techniques have been applied on the clean data to select the preferable model. The decision of the preferable model has to be achieved based on the maximum number of minimum value of the parameters of residual analysis and average error. The preferable model has been used to extract the knowledge from the data mining. The goal of the

Data mining is the process of extracting knowledge from large data sets. It uses artificial intelligence methods to discover the hidden relationships among the huge amount of data that is collected. It has a great potential to improve applications in many fields like Healthcare systems, Customer relationship management, Financial banking, Research analysis, Bio informatics, Marketing analysis, Education, Manufacturing engineering, Criminology and many more. Criminology is the study of crimes and typically a criminologist’s job include analyzing data to determine why the crime was committed and more importantly to predict and prevent criminal behavior in the future. It became an interesting field to apply data mining techniques because of its large datasets and the complexity of relationships between the data. This paper will discuss some of the tools and techniques used in this field to find out important information that will help and support the police forces and reduce social nuisance.