Data Mining And Statistics For Decision Making ...

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Data mining, also known as knowledge discovery in data (KDD), is the process of uncovering patterns and other valuable information from large data sets. Given the evolution of data warehousing technology and the growth of big data, adoption of data mining techniques has rapidly accelerated over the last couple of decades, assisting companies by transforming their raw data into useful knowledge. However, despite the fact that that technology continuously evolves to handle data at a large-scale, leaders still face challenges with scalability and automation.

Data mining has improved organizational decision-making through insightful data analyses. The data mining techniques that underpin these analyses can be divided into two main purposes; they can either describe the target dataset or they can predict outcomes through the use of machine learning algorithms. These methods are used to organize and filter data, surfacing the most interesting information, from fraud detection to user behaviors, bottlenecks, and even security breaches.

When combined with data analytics and visualization tools, like Apache Spark, delving into the world of data mining has never been easier and extracting relevant insights has never been faster. Advances within artificial intelligence only continue to expedite adoption across industries.

The data mining process involves a number of steps from data collection to visualization to extract valuable information from large data sets. As mentioned above, data mining techniques are used to generate descriptions and predictions about a target data set. Data scientists describe data through their observations of patterns, associations, and correlations. They also classify and cluster data through classification and regression methods, and identify outliers for use cases, like spam detection.

1. Set the business objectives: This can be the hardest part of the data mining process, and many organizations spend too little time on this important step. Data scientists and business stakeholders need to work together to define the business problem, which helps inform the data questions and parameters for a given project. Analysts may also need to do additional research to understand the business context appropriately.

3. Model building and pattern mining: Depending on the type of analysis, data scientists may investigate any interesting data relationships, such as sequential patterns, association rules, or correlations. While high frequency patterns have broader applications, sometimes the deviations in the data can be more interesting, highlighting areas of potential fraud.

Decision tree: This data mining technique uses classification or regression methods to classify or predict potential outcomes based on a set of decisions. As the name suggests, it uses a tree-like visualization to represent the potential outcomes of these decisions.

Process mining leverages data mining techniques to reduce costs across operational functions, enabling organizations to run more efficiently. This practice has helped to identify costly bottlenecks and improve decision-making among business leaders.

But, why is statistics needed Knowledge is what we know. Information is the communication of knowledge. Data are known to be crude information and not knowledge by themselves. The sequence from data to knowledge is as follows: from data to information (data become information when they become relevant to the decision problem); from information to facts (information becomes facts when the data can support it); and finally, from facts to knowledge (facts become knowledge when they are used in the successful completion of the decision process). Figure 1 illustrates this statistical thinking process based on data in constructing statistical models for decision making under uncertainties. That is why we need statistics. Statistics arose from the need to place knowledge on a systematic evidence base. This required a study of the laws of probability, the development of measures of data properties and relationships, and so on.

Data mining has been defined in almost as many ways as there are authors who have written about it. Because it sits at the interface between statistics, computer science, artificial intelligence, machine learning, database management and data visualization (to name some of the fields), the definition changes with the perspective of the user:

The main part of data mining is concerned with the analysis of data and the use of software techniques for finding patterns and regularities in sets of data. It is the computer which is responsible for finding the patterns by identifying the underlying rules and features in the data. The choice of a particular combination of techniques to apply in a particular situation depends on both the nature of the data mining task to be accomplished and the nature of the available data. The idea is that it is possible to strike gold in unexpected places as the data mining software extracts patterns not previously discernible or so obvious that no-one has noticed them before. The analysis process starts with a set of data, uses a methodology to develop an optimal representation of the structure of the data during which time knowledge is acquired. Once knowledge has been acquired this can be extended to larger sets of data working on the assumption that the larger data set has a structure similar to the sample data. This is analogous to a mining operation where large amounts of low grade materials are sifted through in order to find something of value.

Hence data warehousing allows the enterprise to remember what it has noticed about its customers. Data warehousing provides the enterprise with a memory. But, memory is of little use without intelligence. That is where data mining comes in. Intelligence allows us to comb through our memories noticing patterns, devising rules, coming up with new ideas to try, and making predictions about the future. The data must be analyzed, understood, and turned into actionable information. Data mining provides tools and techniques that add intelligence to the data warehouse. Data mining provides the enterprise with intelligence. Using several data mining tools and techniques that add intelligence to the data warehouse, an enterprise will be able to exploit the vast mountains of data generated by interactions with its customers and prospects in order to get to know them better.

Let us define the main tasks well-suited for data mining, all of which involve extracting meaningful new information from the data. Knowledge discovery (learning from data) comes in two flavours: directed (supervised) and undirected (unsupervised) learning from data. The six main activities of data mining are:

Background: Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research.

Results: The paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology.

Conclusions: Biomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers.

Data collection is very important to a company like Netflix. It collects data from its customers based on their behavior and past viewing patterns. It uses that information to make recommendations based on their preferences. This is the basis behind the \"Because you watched...\" lists you'll find on your subscription.\"}},{\"@type\": \"Question\",\"name\": \"What Are the 3 Pillars of Data Analytics\",\"acceptedAnswer\": {\"@type\": \"Answer\",\"text\": \"There are three pillars to data analytics. They are the needs of the entity that is using the models, the data and the technology used to study it, and the actions and insights that come as a result of the use of this kind of analysis.\"}},{\"@type\": \"Question\",\"name\": \"What Is Predictive Analytics Good for\",\"acceptedAnswer\": {\"@type\": \"Answer\",\"text\": \"Predictive analytics is good for forecasting, risk management, customer behavior analytics, fraud detection, and operational optimization. Predictive analytics can help organizations improve decision-making, optimize processes, and increase efficiency and profitability. This branch of analytics is used to leverage data to forecast what may happen in the future.\"}},{\"@type\": \"Question\",\"name\": \"What Is the Best Model for Predictive Analytics\",\"acceptedAnswer\": {\"@type\": \"Answer\",\"text\": \"The best model for predictive analytics depends on several factors, such as the type of data, the objective of the analysis, the complexity of the problem, and the desired accuracy of the results. The best model to choose from may range from linear regression, neural networks, clustering, or decision trees.\"}}]}]}] Investing Stocks Bonds Fixed Income Mutual Funds ETFs Options 401(k) Roth IRA Fundamental Analysis Technical Analysis Markets View All Simulator Login / Portfolio Trade Research My Games Leaderboard Economy Government Policy Monetary Policy Fiscal Policy View All Personal Finance Financial Literacy Retirement Budgeting Saving Taxes Home Ownership View All News Markets Companies Earnings Economy Crypto Personal Finance Government View All Reviews Best Online Brokers Best Life Insurance Companies Best CD Rates