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Data mining l The non-trivial extraction of novel, implicit, and actionable knowledge from large datasets. l Extremely large datasets l Discovery of the non-obvious l Useful knowledge that can improve processes l Can not be done manually l Technology to enable data exploration, data analysis, and data visualization of very large databases at a high level of abstraction, without a specific hypothesis in mind. l Sophisticated data search capability that uses statistical algorithms to discover patterns and correlations in data. Data Mining is a step of Knowledge Discovery in Databases (KDD) Process l Data Warehousing l Data Selection l Data Preprocessing l Data Transformation l Data Mining l Interpretation/Evaluation l Data Mining is sometimes referred to as KDD and DM and KDD tend to be used as synonyms Data Mining in CRM l DM helps to l Determine the behavior surrounding a particular lifecycle event l Find other people in similar life stages and determine which customers are following similar behavior patterns more l Building Data Mining Applications for CRM by Alex Berson, Stephen Smith, Kurt Thearling (McGraw Hill, 2000). Data Minin Customer Life Cycle Info. g
Levels of data mining Macro level Mining at the macro level gives us a broad overview of the data. This is used under following circumstance # when we are dealing with the customers for the first time. # when the co. is enterning a new market with a new product. # we are dealing aspects of services which influence a majority of the customer # predicting the possibility of an action that the customer has never taken. Micro (individual level) Relationship with individual customer can be strengthen by offering customized value proposition witch you get from micro level scanning knowledge obtained is useful when the company wants to provide customized service Example: When the firm wants to take advantage of personal events like birthday in a customers life.
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∙ 14y ago
The levels of data mining typically include data collection, data preprocessing, data mining, and interpretation/evaluation of results. These stages involve gathering raw data, cleaning and transforming the data into a suitable format, applying data mining techniques to extract patterns or insights, and interpreting the findings to make informed decisions.
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What are the seminar topics related to data mining?
Some seminar topics related to data mining could include: Introduction to data mining techniques and algorithms Applications of data mining in business intelligence Big data analytics and data mining Ethical considerations in data mining and privacy protection.
What is directed data mining and undirected data mining in database?
Directed data mining involves using predefined goals or objectives to guide the analysis and modeling of data. In contrast, undirected data mining aims to discover patterns or relationships in data without specifying a particular outcome in advance. Directed data mining is typically used for tasks such as classification and regression, while undirected data mining techniques include clustering and anomaly detection.
Different types of data mining?
Some different types of data mining include clustering, classification, regression, association rule mining, and anomaly detection. Clustering involves grouping similar data points together, while classification involves categorizing data into predefined classes. Regression predicts a continuous value based on input variables, and association rule mining uncovers patterns in data sets. Anomaly detection identifies unusual or outlier data points.
What is data reduction in terms of data mining?
Data reduction in data mining refers to the process of reducing the volume of data under consideration. This can involve techniques such as feature selection, dimensionality reduction, or sampling to simplify the dataset and make it more manageable for analysis. By reducing the data, analysts can focus on the most relevant information and improve the efficiency of their data mining process.
How is data mining a key piece of analytic?
Data mining is crucial in analytics as it involves extracting valuable insights and patterns from large datasets. By using data mining techniques, businesses can uncover hidden correlations, trends, and patterns in their data which can then be used to make informed decisions, predict future outcomes, and optimize processes. Ultimately, data mining enables organizations to gain a competitive edge by leveraging their data effectively.
What are the implications for choosing different confidence levels for variability in data mining?
Perhaps the data that you are given.
Characteristics of data mining?
CHARECTERISTICS OF DATA MINING CHARECTERISTICS OF DATA MINING
Distinguish between Data mining and text mining?
mining the data is called data mining. Mining the text is called text mining
Different types of data mining?
Some different types of data mining include clustering, classification, regression, association rule mining, and anomaly detection. Clustering involves grouping similar data points together, while classification involves categorizing data into predefined classes. Regression predicts a continuous value based on input variables, and association rule mining uncovers patterns in data sets. Anomaly detection identifies unusual or outlier data points.
What services do the data mining company provide?
Data Mining companies provide such services as mining for data and mining for data two electric bugaloo. They will often offer to resort to underhanded tactics to mine said data.
How do you put data mining in a sentence?
Data mining can uncover interesting patterns. Some cookies will upload solely for the purpose of data mining.
What are the key relationships between data warehouse and data mining?
Data warehouse is the database on which we apply data mining.
What is data mining in data processing?
Simply, Data mining is the process of analyzing data from several sources and converting it into useful data.
Where can one learn online what is data mining?
One can learn about data mining by visiting the data mining wikipedia page, which has a very comprehensive article about the topic, starting with the etymology and mostly talking about the various uses of data mining.
Which of the following is the computer-assisted process of sifting through and analyzing vast amounts of data to extract meaning and discover new knowledge?
Data mining
What is the difference between OLAP and data mining?
difference between Data Mining and OLAP
What does data mining mean?
The term data mining is generally known as the process of analyzing data from many different perspectives in order to correctly organize the data. Sometimes data mining is also called knowledge dicovery.
