Table of Contents
- A) Utilizing Predictive analytics Understanding the Client Behavior
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- C) Web Mining to discover Business Intelligence from Web Customers
- D) Clustering to find related Customer Information
- 2).Appraisal of Reliability of Data Mining Algorithms
- A) Concerns raised by Consumers
- B) The validity of Privacy Concerns
- C) Addressing Privacy Concerns
- 4) Usage of Predictive Analysis to Gain a Competitive Advantage
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A) Utilizing Predictive analytics Understanding the Client Behavior
The utilization of predictive analytics has effectively enabled applications to reinforce customer recommendations, client value and chain management, fraud detection, as well as campaign optimization of campaigns. The core aim of utilizing predictive analytics to understand client behavior centers on comprehending client behavior, predicting product demand, tracking the performance of clients or products within the market and propelling incremental revenue that enables transformation of data into information, and the subsequent transformation of information into knowledge (Bloodgood & Salisburg, 2001).
B) Associations Discovery in Products sold to Clients
The use of association discovery technology has enabled new forms of information extraction in the field of data mining. Data mining is employed for diverse purposes, and the potential of data mining is almost endless. In leveraging immense repositories of data assembled by entities, data mining techniques such as associations discovery and methods avail unparalleled opportunities in discovering and selling products to clients (Bloodgood & Salisburg, 2001).
C) Web Mining to discover Business Intelligence from Web Customers
Web mining refers to data mining for web data, which enables businesses to turn their immense repositories of transactional and web usage data into actionable knowledge that is essential at each level of the enterprise not merely the front-end of an online store. In leveraging immense repositories of data assembled by corporations, data mining techniques such as web mining methods avail unparalleled opportunities in understanding business processes and in forecasting future behavior. Web mining is critical in coming up with decisions in the business, which enable for further patterns and trends of access by users to content of the web pages and the behavior of customers. Web mining techniques aid in highlighting the patterns involved in the future trends through the utilization of business intelligence. Web mining also avails a solution for business decision problems of E-commerce for retailers web Site solved through Web Mining (Kudyba & Hoptroff, 2001).
D) Clustering to find related Customer Information
Clustering encompasses a technique for grouping a set of abstract or physical objects into categories of similar objects. The choice of a clustering algorithm relies on the form of available data, as well as its purpose and application. For web usage mining, clustering techniques are essentially utilized to discover two forms of useful clusters; page clusters and user cluster (Kudyba & Hoptroff, 2001). User clustering endeavors to highlight groups of users with matching browsing preference and habit. Web page clustering seeks to discover groups of pages that appear to be conceptually connected according to the users’ perception.
2).Appraisal of Reliability of Data Mining Algorithms
Reliability evaluates the manner in which data mining model undertakes diverse data sets. Validation entails the process of verifying how effective the data mining models perform against real data. It is essential to validate the data mining models by assessing their quality and characteristics prior to deploying them into the production environment. The first model entails the utilization of diverse measures of statistical validity to evaluate whether there are challenges within the data or in the model. The second approach entails separating the data into training and testing to determine the accuracy of predictions. The third approach may entail asking business experts to appraise the results of data mining model to determine whether, the highlighted patterns bear the meaning in the targeted business scenario. Fault detection can be regarded as detecting abnormal process behaviors. Error rate relates to cases of misclassified cases related to diverse forms of problem (Kudyba & Hoptroff, 2001).
3) Privacy Concerns relating to the Collection of Personal Data in relation to Mining Purposes
As data mining has progressed, its influence on privacy has overtime become increasingly complex and controversial. Some of the factors that have rendered addressing privacy in relation to data mining more difficult include enhanced availability and minimized cost of data mining tools; enhanced digitization of data and consequential enhancement in the amount of data; enhanced data aggregation, and enhanced utilization of data warehouses as critical repositories for numerous applications (Amooee, et. al., 2011). The mining of personal information has raised privacy concerns, especially with regard to identifying information to create profiles for individuals. Thus, there are privacy concerns in data mining when it comes to using people’s information in creating their profiles.
A) Concerns raised by Consumers
There are a number of concerns for privacy advocacy, which appear to highlight a number of issues. These issues range from concerns whether there is a concise description of a program’s aggregation of personal information; whether the information collected for a certain purpose will be employed for extra secondary purposes; whether the information will be derived subsequent to the collection and utilized to generate dossiers on individuals so as to highlight potential criminals/terrorists and the form of action to be undertaken by government on the grounds of information gleaned from the data mining program. Other concerns include whether there is a sufficient redress system for persons to review and correct their personal information collected and sustained so as to evade repeated “false positives” emanating from data mining program (Jafari & Sheehan, 2003).
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B) The validity of Privacy Concerns
While ICT have become omnipresent within contemporary life, one cannot fail to notice the ethical problems experienced as such technologies evolve. Some of the ethical issues relate to the validity with regard to private information. These issues encompass access to ICT infrastructure and education, contravention of property rights, and information gathering via spying. The concerns on privacy can be considered as valid based on the fact that data mining can easily be abused.
C) Addressing Privacy Concerns
Online business has given rise to several potential ethical issues dwelling on honesty and integrity, responsibility, accountability, protection of data, privacy and confidentiality, and freedom from invasiveness. There is a need, thus, to formulate a concise vision that is likely to give way to suitable values, skills, and professional competencies demanded by intelligent and knowledge-based systems. It is essential to safeguard individuals from privacy intrusion and unjust discrimination. Whereas the concerns raised by data mining are genuine, data mining programs remain critical for enhancing service performance, detecting fraud, abuse, and waste, and detecting criminal activities (Rygielski et. al., 2002). Consumers should be awarded diverse levels of “opt-out” choices such as no data mining admissible, for internal use only, or one suggesting that the information employed is for both internal and external uses.
4) Usage of Predictive Analysis to Gain a Competitive Advantage
Businesses utilize predictive analysis in diverse scenarios in pursuit of competitive edge: first, businesses utilize predictive analysis to broaden the group of clients and enable them employ such data to attain a competitive advantage. Predictive analytics promote a business’ customer Centricity and consequently allowing the business to gain competitive advantage (Büchner & Mulvenna, 1998). This draws from the capability to render timely, meaningful, and relevant offers that propel deeper client loyalty and engagement among the present clients. Client-centric business models highlight individual customer needs and buying patterns (Apte et. al., 2002). Predictive analytics is critical in acquiring, growing, and retaining customers. The internet has dramatically altered the rules for contemporary businesses that face the challenge of enhancing and sustaining performance throughout the enterprise. The advancement witnessed in the World Wide Web and enabling technologies has led to easier data collection, information exchange, and data exchange and has enhanced the speeding up of the bulk of business functions.