Question

Consider which attributes, in your opinion, are features of a successful data mining efforts and summarize, in your own words, what is your own criteria that describe a successful data mining effort.

Answer 1

* Data Mining efforts- Oclassification Analysis: This form of analysis is used classify different dates in different classes classification is a similar to clustering in that it also segments data records into diffrent segments called classes. In classification process, the structure or identity of the data is known. popular example is e-mail to label email as legitimate or as spam, bused on known patterns. @ Clustering :- The opposite of classification, clustering is form of analysis with the structure of the data is discovered as it is processed by being compared to similar data. It is deals more with the unknown, unlike classification. Anomaly or Outlier Detection - It is the process of examining data for errors that may require further evaluation and human intervention to ! eigther use the data data or discard it. ④ Regression Analysis: - The statistical process of estimating the relationship between variables which helps you understand the characteristic value of the dependent variable changes. It is genroully used for predictions, it helps to determine it any one of the independent variables is varied, so if you change one variable, a separate variable is affected. ② Prediction Rules - This technique is a chat data mining is all about. It uses past data to the predict future actions or behaviors. Summarization : summarization is a present mode compact representation of the data set, throughly processed and modeled to give a clear overview of the results.

Sequential patterns :- One of the many forms of data mining, sequential patterns are specifically designed to discover a sequential series of the events. It is one of the more common forms of mining as data by default is recorded sequenticuly, such as sales patterns over the course of the day.. statistical Techniques :- While most dades mining techniques focus on prediction based on past dada, statistics for focuses on probabilistic models, specifically inference. & Visualization: - It is the process of conveying information that has been processed in a simple to understand visual form, such as charts, graphs, digital images, and animation. 16 Neural Networks :- The neural network data mining is the process of gathering and extracting data by recognizing existing patterns in a database using an artificial neural network Data klarehousing You cannot have data mining without data warehousing. 7 is the databases where structured data resides and is processed and prepared for mining. It does the tersk of sorting data, classifying it, discording unusable data and setting up metadata. & Criteria for sucessful data mining: - o clear business goods the company aims to achieve using the daten mining ② Relevancy of data sources to avoid duplicates and animportant resuts. & Completeness of data to ensure all the essential information is covered. his daten analysis besults to meet ♡ Applicability of big dates analysis besults to meet the the tentu yools specified Customer engagement and bottom line growth as the indicator indicators of data mining sucess.