Data Mining Research Paper

A set of experiments have been conducted and proved that the graph database provides a promising result. The graph approach is applied in two different methods. The sub graph approach and path finding approach. In the sub graph approach the data structures that repeat often are compared where as in path finding approach finite length search is performed. Data in the databases are written using various methods with ILP (Inductive Logic Programming) being prominent. Concept discovery involves searching for the target data given a background of facts. Association rule mining is used in relational concept discovery. Association rule mining is finding frequent patterns, associations or correlations among sets of items or objects in databases. Relational association rules are expressed as query extensions in first-order logic. Hence in the method we present a hybrid graph-based discovery of data involving both graph substructure method and path finding

The output of an association rule mining algorithm is a set of association rules respecting the user-specified minsup and minconf thresholds.

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.

Having data is not valuable but using data is. Analytic insights are changing the way corporates strategize and also redefining customer expectations. Analytics is the new differentiator between success and failure in the cut throat e-commerce and internet services based industry. The huge proportions of data generated from the increasing number of smart phones, the social networks and the ever more penetrating internet are automating customer centric marketing and other services. The idea is to predict what a customer may want to buy even before the customer realizes what they need. The techniques to achieve these results are broadly classified as Predictive Analytics.

Data mining software allows users to analyze large databases to solve business decision problems. Data mining is, in some ways, an extension of statistics, with a few

Apriori algorithm is one of the notable works for mining multidimensional association rules. A recent study of Khare et al. [3] has implemented multidimensional association rules using Boolean relational calculus to discover frequent predicate sets. For the purpose of retrieving pattern from database, the relational database is first transformed into Boolean matrix by setting us a Boolean matrix Am*n , where m (are records) and n ( are different dimension

Although data mining is still in its infancy, companies in a wide range of industries –

However, after extracting the information from a large database, the data are analyzed and summarized into useful information. This process of analyzing and summarizing the extracted data is known as Data Mining (Maimom & Rokach, 2007). In fact, data mining is one of the important steps of KDD process that infer algorithms, explore data, develop model, and discover previous patterns (Maimom & Rokach). Hence, due to the accessibility and abundance of data, knowledge discovery and data mining have become considerably important in the healthcare industry (Maimom & Rokach).

1.3 Literature Review on Seven Data Mining Techniques . . . . . . . . . . . . . . . . . 6

This research paper highlight the importance and need of data mining in the age of electronic media where large amount of information and consolidated database is readily available. This seemingly useful information can unearth some mind-blowing statistics and predict the future trends with relative ease through use of data mining techniques which can benefit the businesses, start-ups, country and individual alike. However, since data mining is effective in bringing out patterns, alerts, correlation and association through complex algorithms and analysis, it has, over the past few decades proved to be a useful

This research paper is about the Comparative analysis of three data mining software’s selected based on four important criteria Performance, Functionality, Usability and Ancillary Tasks support. “Data Mining is a field of study that is gaining importance and is used to explore data in search of patterns or relationships between variables and is applied to new data used for predictions”. (Statistics – Textbook. (n.d.). Retrieved November 17, 2015). Selection of the appropriate data mining tools is critical to any research or business and this could impact the business in terms of money, resources and time. Data experts

In the year of 2001 when the use of data mining in marketing was a relatively new concept Shaw ,Subramaniam, Tan and Welge gave an insight about management of large database using data mining techniques. They brought the concept of identifying useful information from the large customer database by identifying hidden patterns. They integrated data mining and marketing knowledge management to help in managing marketing decisions.

Data mining is the process of discovering patterns, trends, correlations from large amounts of data stored electronically in repositories, using statistical methods, mathematical formulas, and pattern recognition technologies (Sharma n.d.). The main idea is to analyze data from different perspectives and discover useful trends, patterns and associations. As discussed in the previous chapter, the healthcare organizations are producing massive amounts of electronic medical records, which are impossible to process using traditional technologies (e.g., Microsoft excel). Therefore data mining is becoming very popular in this field as it can be used to identify the presence of chronic disease, detect the cause of the disease, analyze the effectiveness of treatment methods, predict different medical events, identify the side effects of the drugs, and so on. Kidney diseases such as CKD or AKI require immediate detection and medical attention based on the patient’s clinical condition, medical history, medication history and some demographical factors. From the literature survey, we discovered a good number of studies and tools that used data mining methods such as clustering, association, and classification to improve the decision-making ability of the healthcare providers regarding kidney disease. In the subsequence sections in this chapter, we present an overview of the data mining methods and discuss how they have been used in existing literature.

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

This technical paper consists of the study of data mining algorithm in cloud computing. Cloud Computing is an environment created in user’s machine from online application stored in clouds and run through web browser. Therefore, it is essential to manage user’s data efficiently. Data mining also known as knowledge discovery is the process of analyzing data from different perspectives and summarizing it into useful information where the information can be used to increase revenue, cut costs of implementation and maintenances, or all. Data mining software and/or algorithms 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. The process of mining data can be done in many ways; this paper discusses the theoretical study of two algorithms K-means and Apriori, their explanation using flow chart and pseudo code, and comparison for time and space complexity of the two for the dataset of an “Online Retail Shop”.