Data mining: concepts and techniques / Jiawei Han, Micheline Kamber, Jian Pei. . A Business Analysis Framework for Data Warehouse Design The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor Morgan Data Warehouse and OLAP Technology for Data Mining. Data Mining: Concepts and Techniques. Home · Data Mining: Concepts and Techniques Author: Jiawei Han | Micheline Kamber.
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Authors: Jiawei Han Micheline Kamber Jian Pei. Hardcover eBook ISBN: . Data warehouse engineers, data mining professionals, database. [Jiawei Han; Micheline Kamber; Jian Pei] -- The increasing volume of data in Although advances in data mining technology have made extensive data collection much Data Warehousing and Online Analytical Processing; Chapter 5. Data mining: concepts and techniques by Jiawei Han and Micheline Kamber The tools it provides assist as data warehouses and the World Wide us in the.
And follow the traditional life cycle of any da,ta science project. Da,ta can be used for many purposes: — quality assurance — to find actionable patterns stock trading, fraud detection — for resale to your business clients — to optimize decisions and processes operations research — for investigation and discovery IRS, litigation, fraud detection, root cause analysis — machine-to-machine communication automated bidding systems, automated driving — predictions sales forecasts, growth, and financial predictions, weather Embrace light analytics.
Leverage the power of compound metrics: KPIs derived from da,tabase fields, that have a far better predictive power than the original d,atabase metrics. For instance, your da,tabase might include a single keyword field but does not discriminate between the user query and search category sometimes because d,ata comes from various sources and is blended together.
Detect the issue, and create a new metric called keyword type — or d,ata source. Another example is IP address category, a fundamental metric that should be created and added to all digital analytics projects.
When do you need true real-time processing? When fraud detection is critical, or when processing sensitive transactional d,ata credit card fraud detection, calls. Other than that, delayed analytics with a latency of a few seconds to 24 hours is good enough. Make sure your sensitive d,ata is well protected.
Make sure your algorithms cannot be tampered by criminal hackers or business hackers spying on your business and stealing everything they can, legally or illegally, and jeopardizing your algorithms — which translates in severe revenue loss. An example of business hacking can be found in section 3 in this article. Blend multiple models together to detect many types of patterns. Average these models. Data mining main objectives Data mining has proved its existence as one of the successful solutions for the analysis of large amounts of data and turns it from the accumulated and incomprehensible data to valuable information which can be exploited to take advantage of it as knowledge.
The main objectives of using data mining, however, can be classified in the following points: 1. To explain some of observed phenomena, for example why the proportion of smokers increases in the country in the recent years? To verify a theory, for example: validation of the theory which says that large families interested in health insurance rather than small families.
Data mining : concepts and techniques
To analyze the data for new and unexpected relationships, for example: how will be the public expenditure that was inherent in the deception operations in a wide range of credit cards. Data mining concept Data mining is a computerized or a manual search for knowledge from huge historical data without prior assumptions about what can be defined.
It is an analytical process to explore and search a huge database to extract useful patterns and relationships and to find the correlation between its elements. Data mining is a new technology that enables the predictive pattern discovery, hypothesis creation and testing, and insight-provoking generation. Data mining which is also known as Knowledge Discovery in Database KDD is any application which has a capability for extracting hidden knowledge and it is not related to any specific industry.
It is considered as one of the top ten information technology aspects that will change the world in the coming years. Data mining process blends between artificial intelligence science, statistics, machine learning and databases. Why mining data?
We stand daily in long line at big supermarkets waiting for our turn to pay the value of some of our purposes which we have downloadd. During this period of waiting we hear multiple beeps coming out from many barcode readers. We know that any barcode beep is a transaction, and represented by a download record stored in the database.
So, hundred thousands of records can be accumulated in the database per day. These records, however, contain important information of download process for many items and also the best-selling items.
But how can we benefit from all of this data and how to make it useful in our business? Data mining technology only can answer this question. The problem is not like the past concentrated in the lack of storage space and insufficient data but, in fact, our lack in the experiences that is capable to convert this data to a valuable knowledge.
As we said before, data mining technology is the process of extrapolation in a large volume of data in order to detect influencing factors of a particular behavior such as the causes, conditions, contraindications etc.
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Data mining is not directed to a specific domain but it has a clear impact in all life aspects. So, we can summarize its importance in information industry and why we use it in the following points: - Eextraction of useful patterns become important in light of rapid development and widespread dissemination of databases. Data mining techniques capable to answer complex questions in record time, especially the types of questions those has been difficult to find answers to them by using classical statistical techniques.
The process of knowledge discovery The discovery of knowledge in a database is a process connected with managers and decision makers who are involved in results implementation. The KDD process consists of seven stages and can be summarized as follows: 1.
Data Mining: Concepts and Techniques,
Machine Learning for Data Streams. Albert Bifet. Deep Learning with Hadoop. Dipayan Dev. A General Introduction to Data Analytics. Data Mining and Analysis in the Engineering Field. Vishal Bhatnagar. Mastering Java Machine Learning.
Uday Kamath. Guozhu Dong. Handbook of Big Data Technologies. Albert Y.
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Data Mining: Concepts and Techniques
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Tanja E. Measurement, Modelling and Evaluation of Computing Systems. Reinhard German. Tal Malkin. AI Advances in Artificial Intelligence. Byeong Ho Kang. Information and Communications Security. Lucas C. Howon Kim. Algorithmic Aspects of Cloud Computing.
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