![\"Data](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Data-Mining-Process-01-1.jpg\")
<\/a>Data Mining Tutorial – Data Mining Process<\/p><\/div>\n
d. Data Transformation<\/h4>\n
It is also a data consolidation method. Also, it\u2019s a phase in which the selected data is transformed<\/span> into forms. That is appropriate for the mining procedure.<\/p>\ne. Data Mining<\/h4>\n
In this, we have to apply clever techniques to extract patterns potentially<\/span> useful.<\/p>\nf. Pattern Evaluation<\/h4>\n
In this process, interesting patterns representing knowledge are identified<\/span> based on given measures.<\/p>\ng. Knowledge Representation<\/h4>\n
It is the final phase. Particularly in this phase, knowledge is discovered<\/span> and represented to the user. This essential step uses visualization techniques. That help users understand and interpret the data mining results.<\/p>\nCategories of Data Mining Systems<\/h3>\n
As there are too many data mining systems available, but in this Data Mining Tutorial, we will study 4 major classifications. Also, some systems are specific t<\/span>hat we need to dedicate to a given data source. Further, according to various criteria, data mining systems have to categorize.<\/p>\na. Classification according to the type of data source mined<\/strong><\/p>\nAccording to the type of data handle, have to perform classification of data mining. Such as spatial data, multimedia data, time-series data, text data, World Wide Web, etc.<\/p>\n
b. Classification according to the data model drawn on<\/strong><\/p>\nIn this classification is done<\/span> on the basis of a data model. Such as a relational database, object-oriented database, data warehouse, transactional, etc.<\/p>\nc. Classification according to the king of knowledge discovered<\/strong><\/p>\nIn this classification, it is been done on the basis of the kind of knowledge. Such as characterization, discrimination, association, classification, clustering, etc.<\/p>\n
d. Classification according to mining techniques used<\/strong><\/p>\nAs data mining systems employ<\/span> are used<\/span> to provide different techniques. According to the data analysis, we have to do this classification. Such as machine learning, neural networks, genetic algorithms, \u00a0etc.<\/p>\nData Mining Issues<\/h3>\n
In this part of the Data Mining Tutorial, we will discuss some major issues we faced in it.<\/p>\n
a. Mining Methodology<\/span>\u00a0Issues<\/h4>\nThese issues to the data mining approach applied and their limitations such as the versatility of the mining approaches that can dictate mining methodology<\/span> choices.<\/p>\nb. Performance Issues<\/h4>\n
As there is much artificial intelligence and statistical methods exist. That is use for data analysis. However<\/span>, these methods were often not designed for the very large datasets. And data mining is dealing with today. As Terabyte sizes are common.<\/p>\nWe can say this raises the issues of scalability and efficiency of the data mining methods. That would process considerably<\/span> large data. . Moreover, Linear algorithms are usually the norm. In the same theme, sampling can be used<\/span> for mining instead of the whole dataset.<\/p>\nHowever<\/span>, issues like completeness and choice of samples may arise. Other topics in the issue of performance are incremental updating and parallel programming. We use parallelism to solve the size problem. And if the dataset can be subdivided<\/span> and the results can be merged<\/span> later.<\/p>\nIncremental updating is important for merging results from parallel mining. That the new data becomes available without having to re-analyze the complete dataset.<\/p>\n
c. Data Source Issues<\/h4>\n
We must know that there are many issues related to the data sources. Some are practical such as the diversity of data types. While others are philosophical like the data glut problem.<\/p>\n
We certainly<\/span> have an excess of data since. Also, we already have more data than we can handle. Then we are still collecting data at an even higher rate. Although, If the spread of database management systems.<\/p>\nThat has helped in increasing the gathering of information. And the advent of data mining is certainly<\/span> encouraging more data harvesting. The current practice is to collect as much data as possible now and process it or try to process it, later.<\/p>\nRegarding<\/span> the practical issues related to data sources, there is the subject databases. Thus, we need to focus on diverse complex data types. We are storing different types of data in a variety of repositories. It is difficult to expect a data mining system to achieve good mining results on all kinds of data and sources.<\/p>\nAs different kinds of data and sources may require<\/span> distinct algorithms and methodologies. Currently, there is a focus on relational databases and data warehouses.<\/p>\nIt\u2019s a versatile data mining tool, for all sorts of data, may not be realistic. Moreover, data sources, at structural and semantic levels, poses important challenges. That is not only to the database community but also to the data mining community.<\/p>\n
Data Mining Applications<\/h3>\n\n- Weather forecasting.<\/li>\n<\/ul>\n
\n- E-commerce.<\/li>\n<\/ul>\n
\n- Self-driving cars.<\/li>\n<\/ul>\n
\n- Hazards of new medicine.<\/li>\n<\/ul>\n
\n- Space research.<\/li>\n<\/ul>\n
\n- Fraud detection.<\/li>\n<\/ul>\n
\n- Stock trade analysis.<\/li>\n<\/ul>\n
\n- Business forecasting.<\/li>\n<\/ul>\n
\n- Social networks.<\/li>\n<\/ul>\n
\n- Customers likelihood.<\/li>\n<\/ul>\n
More applications include:<\/strong><\/p>\n\n- A credit card company can leverage its vast warehouse of customer transaction data. As we perform this to identify customers. It shows more interest <\/span>in a new credit product.<\/li>\n
- Moreover, we use small test mailing. So the attributes of customers with an affinity for the product have to identify. Recent projects have indicated more than a 20-fold decrease in costs. That is a target for mailing campaigns over conventional approaches.<\/li>\n
- As a diversified transportation company used to apply data mining. That is to identify the best prospects for its services. Further, need to apply this segmentation to a general business database. Such as those provided by Dun & Bradstreet can yield a prioritized list of prospects by region.<\/li>\n
- Large consumer packaged goods company. That can apply data mining to improve its sales process to retailers. Although, data from consumer panel and competitor activity have to apply. That is to understand the reasons for brand and store switching.<\/li>\n
- Through this analysis, we have to the manufacturer it. Then select promotional strategies that best reach their target customer segments.<\/li>\n<\/ul>\n
Areas where Data Mining had Good and Bad Effects<\/h3>\n
a. Good Effects<\/strong><\/p>\n\n- Predict future trends, customer purchase<\/span> habits<\/li>\n
- Help with decision making<\/li>\n
- Improve company revenue and lower costs<\/li>\n
- Market basket analysis<\/li>\n
- Fraud detection<\/li>\n<\/ul>\n
b. Bad Effects<\/strong><\/p>\n\n- User privacy\/security<\/li>\n
- Amount of data is overwhelming<\/li>\n
- Great cost at an implementation stage<\/li>\n
- Possible misuse of information<\/li>\n
- The possible inaccuracy of data<\/li>\n<\/ul>\n
Data Mining Advantages and Disadvantages<\/h3>\na. Data Mining Advantages<\/h4>\n\n- To find probable defaulters, we use data mining in banks and financial institutions. This is done<\/span> based on past transactions, user behavior and data patterns.<\/li>\n<\/ul>\n
\n- It helps advertisers to push right advertisements to the internet. That surfer on web pages based on machine learning algorithms. This way data mining benefit both possible buyers as well as sellers of the various products.<\/li>\n<\/ul>\n
\n- The retail malls and grocery stores peoples used data mining. That is to arrange and keep most sellable items in the most attentive positions. It has become possible due to inputs obtained from data mining software. This way data mining helps in increasing revenue.<\/li>\n<\/ul>\n
\n- As data mining is having<\/span> different methods. That is cost-effective compared to other applications.<\/li>\n<\/ul>\n
\n- We use data mining in so many areas. Such as bio-informatics, medicine, genetics, etc.<\/li>\n<\/ul>\n
\n- We use data mining to identifying criminal suspects. That is by law enforcement agencies as mentioned above.<\/li>\n<\/ul>\n
b. Data Mining Disadvantages<\/h4>\n\n- Security: The time at which users are online for various uses, must be important. They do not have security systems in place to protect us.<\/li>\n<\/ul>\n
\n- As some of the data mining analytics use software. That is difficult to operate. Thus they require<\/span> a user to have knowledge based training.<\/li>\n<\/ul>\n
\n- The techniques of data mining are not 100% accurate. Hence, it may cause serious consequences in certain conditions.<\/li>\n<\/ul>\n
So, this was all about Data Mining Tutorial. Hope you like our explanation.<\/p>\n
Conclusion<\/h3>\n
As a result, we have studied Data Mining introduction. Also, have studied about it’s all concepts. We have covered each and everything with pros-cons and applications. Furthermore, if you feel any query regarding Data Mining tutorial, feel free to ask in a comment section.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this Data Mining Tutorial, we will study\u00a0what is Data Mining. Also, will study data mining scope, foundation, data mining techniques and terminologies in Data Mining. As we study this, will learn data mining...<\/p>\n","protected":false},"author":6,"featured_media":34214,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18],"tags":[3337,3339,3349,3351,3361,16565,7015],"class_list":["post-7149","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-mining","tag-data-mining-advantages","tag-data-mining-applications","tag-data-mining-disadvantages","tag-data-mining-features","tag-data-mining-limitations","tag-data-mining-tutorial","tag-introduction-to-data-mining"],"yoast_head":"\n
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<\/a>