Data Mining Essay

It has recently come to my attention that Target Co. utilizes data mining to extract a wide spectrum of information about its customers by accumulating, analyzing and storing data about customer purchases. While I understand that this practice enables Target Co. to simultaneously deliver individually targeted advertisements across its diverse customer groupings, thereby increasing the potential for sales and improving customer retention, I also understand that large amounts of data unearthed by data mining can be manipulated to uncover hidden purchasing patterns to predict and shape future purchase decisions. Therefore, although there are significant benefits to using data mining, there are also serious costs associated with data mining that

It became an issue for consumer, and they felt uncomfortable due to companies invaded consumers privacy without their consent. Another issue regarding data mining is there no clear policies around how the company uses data once they have collected the information. After Target received negative publicity regarding its original data mining approach, they change their strategy by mixing all their ads, so it not that obvious that they were spying on them. It makes customers’ to assume that everyone else gets same coupons and ads. Data mining has given Target awareness into the relationship between product sales and online reviews. Target retailers identify guests how write reviews, examining their history of purchasing and invited to write review on their products. This program was highly successful, leading Target to obtain incremental reviews and increase sales of those key products. More importantly, data mining has empowered companies to find new opportunities for growth, make better decisions to succeed business goals and safe money. The retailer industry utilizes data mining and customer analytics to support customer decisions. Exploring how collected data can be manipulated to identify customers buying patterns and increase profitability is a growing business

As a corporate executive of a banking institution, the first step to deciding on implementing a data-mining program should be to identify the goal of the data-mining program. The goal could be to determine the potential value of a customer, or to selling the right plan to the right customer at the right time, etc.… It is also important to determine the commercial value of such goal, or how will the bank benefit from it. With clear and reasonable incentives, the executive can then examine the relevant data and/or how to collect relevant data. Keeping in mind the discussion of ethical considerations before consumers gets irritated and turns away from the institution. More importantly, the executive should strictly abide the law to avoid and breach of federal restrictions.

Data mining can cause more problems than it worth. It can be a useful marketing tool for businesses, but at the risk of major privacy threats. If the results can include security issues, false information, and cause inefficiencies on both ends, then it needs to be considered and improved. With the proposed solutions, data mining can become more secure, a less of a misguided problem. The solutions are extremely reasonable, it is just a matter of the government and data mining companies putting in effort to make them

Dana and Gandy (2002) studied the technical and social concerns associated with using data mining techniques to create customer profiles that may exclude classes of consumers from the marketplace. Dana and Gandy’s (2002) study differ from Scott (2014) and Bond and Foss (2005) because it takes into consideration the specific use of data mining techniques employed by companies such neural networks, decision trees, market basket analysis, and clustering. The solution Dana and Gandy (2002) propose is to use more sensitive alternatives to the use of data mining. Dana and Gandy (2002) employ the use of hypothetical scenarios to illustrate how different data usages negatively affect people to simply a complex topic, which is similar to the persuasive devices used by both Scott (2014) and Bond and Foss

Data is often far from perfect. Most of the data mining technique can tolerate and bare some level of imperfection and inconsistency in the data. This imperfection and inconsistency may cause loss of individual’s private data. Preserving privacy data mining focuses on obtaining positive result analysis. The data quality issues that often need to be addressed includes the presence of noise and outliers, missing inconsistent or duplicate data, biased and unrepresentative phenomenon and that the data is supposed to be. For preprocessing and pattern recognition it is mandatory to build the data in representable form before providing it to data mining techniques. In this paper we introduce Noise cure framework as a bridge between data preprocessing and data mining for preprocessing the raw data to bring in a form suitable for pattern recognition in preserving privacy data mining

Data mining or Knowledge Discovery in Databases (KDD) is discovering patterns from large data groups through methods of artificial intelligence, machine learning ,statistics, and database systems. The aim of data mining process is to extract information from a data group and switch it to an ideal format for future . The data mining process comprise of database and data management aspects, data preprocessing, inference, complexity of discovered structures, and updating.

The way a business market or service determines their revenue, sustainability, and longevity. In the past business and entrepreneurs advertised to their customers by going door-to-door or if they were financially stable they would pay for an advertisement on television. Unfortunately, advertising door-to-door would limit coverage and commercials pricing may start at 500. Therefore, a new venture was becoming and reaching more customers called data mining. Now all the managers have to do is figure out the statistic of the population they want to advertise too and market to a specific group of people with their previous purchases. They can use data visualization to analyze customer’s preference and patterns.

In today’s business world, information about the customer is a necessity for a businesses trying to maximize its profits. A new, and important, tool in gaining this knowledge is Data Mining. Data Mining is a set of automated procedures used to find previously unknown patterns and relationships in data. These patterns and relationships, once extracted, can be used to make valid predictions about the behavior of the customer.

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].

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

Data mining is a new technology which could be used in extracting valuable information from data warehouses and databases of companies and governments. It involves the extraction of hidden information from some raw data. It helps in detecting inconsistency in data and predicting future patterns and attitude in a highly proficient way. Data mining is implemented using various algorithm and framework, and the automated analysis provided by this algorithm and framework go ahead of evaluation in dataset to providing solid evidences that human experts would not have been able to detect due to the fact that they

We can be provided by online media a chance for the set and usage of dactylology presentations about various subjects. Nowadays, online media such as locating dactylology videos is based on the existence and correctness of tags,

It is a process of analyzing the relationship among the data from various perspectives and summarizing it into valuable information. It also assists the banks to look for hidden patterns in a group and discovers unknown relationships in the data. These data mining techniques facilitate useful data interpretations for the banking sector to avoid customer attrition. An accurate prediction on the credit approval is important to prospective homeowners, developers, investors, appraisers, tax assessors and other real estate market participants without fraudulence. People who are looking to buy a new place or thing, tend to be more conservative with their budget and acquiring loans from financial institutions. The credit functionality is prime for any banking system over the tentative market conditions. The lack of general credit review system & precise methods in banks are the important reasons, why an expert support system is necessary.

There are growing researches in data mining as a part of education. This new developing field, called Educational Data Mining, concerns with creating techniques that find information from data originate from educational situations. The data can be collected structure verifiable and operational data dwell in the databases of educational establishments. The understudy data can be close to home or scholastic. Additionally it can be gathered from e-learning frameworks which have a vast measure of data utilized by mostly organizations. Educational data mining utilized numerous strategies, for example, decision trees, neural systems, k-nearest Neighbor, Naive Bayes, help vector machines and numerous