about:blank Data Mining Definition
What Is Data Mining?
Data mining is a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers to develop more effective marketing strategies, increase sales and decrease costs. Data mining depends on effective data collection warehousing and computer processing.
Data mining processes are used to build machine learning models that power applications including search engine technology and website recommendation programs.
Data mining is a process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems
about:blank Data Mining Definition
Example of Data Mining
Grocery stores are well-known users of data mining techniques. Many supermarkets offer free loyalty cards to customers that give them access to reduced prices not available to non-members. The cards make it easy for stores to track who is buying what, when they are buying it and at what price. After analyzing the data, stores can then use this data to offer customers coupons targeted to their buying habits and decide when to put items on sale or when to sell them at full price.
Data mining can be a cause for concern when a company uses only selected information, which is not representative of the overall sample group, to prove a certain hypothesis.
- Data mining is the process of analyzing a large batch of information to discern trends and patterns.
- Data mining can be used by corporations for everything from learning about what customers are interested in or want to buy to fraud detection and spam filtering.
- Data mining programs break down patterns and connections in data based on what information users request or provide.
How Data Mining Works
Data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends. It can be used in a variety of ways, such as database marketing, credit risk management, fraud detection spam Email filtering, or even to discern the sentiment or opinion of users. https://2c318f37bcee5563f33d761566a9544b.safeframe.googlesyndication.com/safeframe/1-0-38/html/container.html
The data mining process breaks down into five steps. First, organizations collect data and load it into their data warehouses. Next, they store and manage the data, either on in-house servers or the cloud. Business analysts, management teams and information technology professionals access the data and determine how they want to organize it. Then, application software sorts the data based on the user’s results, and finally, the end-user presents the data in an easy-to-share format, such as a graph or table
DATA MINING TECHNIQUES
1. Data cleaning and preparation
Data cleaning and preparation is a vital part of the data mining process. Raw data must be cleansed and formatted to be useful in different analytic methods. Data cleaning and preparation includes different elements of data modeling, transformation data migration, ETL, ELT, data integration and aggregation. It’s a necessary step for understanding the basic features and attributes of data to determine its best use.
The business value of data cleaning and preparation is self-evident. Without this first step, data is either meaningless to an organization or unreliable due to its quality. Companies must be able to trust their data the results of its analytics, and the action created from those results.
These steps are also necessary for data quality and proper data governance
2. Tracking patterns
Tracking patterns is a fundamental data mining technique. It involves identifying and monitoring trends or patterns in data to make intelligent inferences about business outcomes. Once an organization identifies a trend in sales data, for example, there’s a basis for taking action to capitalize on that insight. If it’s determined that a certain product is selling more than others for a particular demographic, an organization can use this knowledge to create similar products or services, or simply better stock the original product for this demographic.
Classification data mining techniques involve analyzing the various attributes associated with different types of data. Once organizations identify the main characteristics of these data types, organizations can categorize or classify related data. Doing so is critical for identifying, for example, personally identifiable information organizations may want to protect or redact from documents.
Association is a data mining technique related to statistics. It indicates that certain data (or events found in data) are linked to other data or data-driven events. It is similar to the notion of co-occurrence in machine learning, in which the likelihood of one data-driven event is indicated by the presence of another.
The statistical concept of correlation is also similar to the notion of association. This means that the analysis of data shows that there is a relationship between two data events: such as the fact that the purchase of hamburgers is frequently accompanied by that of French fries.
5. Outlier detection
Outlier detection determines any anomalies in datasets. Once organizations find aberrations in their data, it becomes easier to understand why these anomalies happen and prepare for any future occurrences to best achieve business objectives. For instance, if there’s a spike in the usage of transactional systems for credit cards at a certain time of day, organizations can capitalize on this information by figuring out why it’s happening to optimize their sales during the rest of the day.
Clustering is an analytics technique that relies on visual approaches to understanding data. Clustering mechanisms use graphics to show where the distribution of data is in relation to different types of metrics. Clustering techniques also use different colors to show the distribution of data.
Graph approaches are ideal for using cluster analytics. With graphs and clustering in particular, users can visually see how data is distributed to identify trends that are relevant to their business objectives.
Regression techniques are useful for identifying the nature of the relationship between variables in a dataset. Those relationships could be causal in some instances, or just simply correlate in others. Regression is a straightforward white box technique that clearly reveals how variables are related. Regression techniques are used in aspects of forecasting and data modelling
Prediction is a very powerful aspect of data mining that represents one of four branches of analytics. Predictive analytics use patterns found in current or historical data to extend them into the future. Thus, it gives organizations insight into what trends will happen next in their data. There are several different approaches to using predictive analytics. Some of the more advanced involve aspects of machine learning and artificial intelligence. However, predictive analytics doesn’t necessarily depend on these techniques —it can also be facilitated with more straightforward algorithms.
9. Sequential patterns
This data mining technique focuses on uncovering a series of events that takes place in sequence. It’s particularly useful for data mining transactional data. For instance, this technique can reveal what items of clothing customers are more likely to buy after an initial purchase of say, a pair of shoes. Understanding sequential patterns can help organizations recommend additional items to customers to spur sales.
10. Decision trees
Decision trees are a specific type of predictive model that lets organizations effectively mine data. Technically, a decision tree is part of machine learning, but it is more popularly known as a white box machine learning technique because of its extremely straightforward nature.
A decision tree enables users to clearly understand how the data inputs affect the outputs. When various decision tree models are combined they create predictive analytics models known as a random forest. Complicated random forest models are considered black box machine learning techniques, because it’s not always easy to understand their outputs based on their inputs. In most cases, however, this basic form of ensemble modeling is more accurate than using decision trees on their own.
11. Statistical techniques
Statistical techniques are at the core of most analytics involved in the data mining process. The different analytics models are based on statistical concepts, which output numerical values that are applicable to specific business objectives. For instance, neural networks use complex statistics based on different weights and measures to determine if a picture is a dog or a cat in image recognition systems.
Statistical models represent one of two main branches of artificial intelligence. The models for some statistical techniques are static, while others involving machine learning get better with time.
Data visualizations are another important element of data mining. They grant users insight into data based on sensory perceptions that people can see. Today’s data visualizations are dynamic, useful for streaming data in real-time, and characterized by different colors that reveal different trends and patterns in data.
Dashboards are a powerful way to use data visualizations to uncover data mining insights. Organizations can base dashboards on different metrics and use visualizations to visually highlight patterns in data, instead of simply using numerical outputs of statistical models.
13. Neural networks
A neural network is a specific type of machine learning model that is often used with AI and deep learning Named after the fact that they have different layers which resemble the way neurons work in the human brain, neural networks are one of the more accurate machine learning models used today.
Although a neural network can be a powerful tool in data mining, organizations should take caution when using it: some of these neural network models are incredibly complex, which makes it difficult to understand how a neural network determined an output.
14. Data warehousing
Data warehousing is an important part of the data mining process. Traditionally, data warehousing involved storing structured data in relational database management systems so it could be analyzed for business intelligence, reporting, and basic dashboarding capabilities. Today, there are cloud data warehouses and data warehouses in semi-structured and unstructured data stores like Hadoop. While data warehouses were traditionally used for historic data, many modern approaches can provide an in-depth, real-time analysis of data.
15. Long-term memory processing
Long term memory processing refers to the ability to analyze data over extended periods of time. The historic data stored in data warehouses is useful for this purpose. When an organization can perform analytics on an extended period of time, it’s able to identify patterns that otherwise might be too subtle to detect. For example, by analyzing attrition over a period of several years, an organization may find subtle clues that could lead to reducing churn in finance.
16. Machine learning and artificial intelligence
Machine learning and artificial intelligence (AI) represent some of the most advanced developments in data mining. Advanced forms of machine learning like deep learning offer highly accurate predictions when working with data at scale. Consequently, they’re useful for processing data in AI deployments like computer vision, speech recognition, or sophisticated text analytics using Natural Language Processing. These data mining techniques are good for determining value from semi-structured and unstructured data.
DATA MINING TOOLS
Data Mining Tools
|Data Mining tools have the objective of discovering patterns/trends/groupings among large sets of data and transforming data into more refined information.It is a framework, such as Rstudio or Tableau that allows you to perform different types of data mining analysis.We can perform various algorithms such as clustering or classification on your data set and visualize the results itself. It is a framework that provides us better insights for our data and the phenomenon that data represent. Such a framework is called a data mining tool.The Market for Data Mining tool is shining: as per the latest report from ReortLinker noted that the market would top $1 billion in sales by 2023, up from $ 591 million in 2018These are the most popular data mining tools:|
Data Mining Tools
|1. Orange Data Mining: Orange is a perfect machine learning and data mining software suite. It supports the visualization and is a software-based on components written in Python computing language and developed at the bioinformatics laboratory at the faculty of computer and information science, Ljubljana University, Slovenia.As it is a software-based on components, the components of Orange are called “widgets.” These widgets range from preprocessing and data visualization to the assessment of algorithms and predictive modeling.Widgets deliver significant functionalities such as:Displaying data table and allowing to select featuresData readingTraining predictors and comparison of learning algorithmsData element visualization, etc.Besides, Orange provides a more interactive and enjoyable atmosphere to dull analytical tools. It is quite exciting to operate.Why Orange?Data comes to orange is formatted quickly to the desired pattern, and moving the widgets can be easily transferred where needed. Orange is quite interesting to users. Orange allows its users to make smarter decisions in a short time by rapidly comparing and analyzing the data.It is a good open-source data visualization as well as evaluation that concerns beginners and professionals. Data mining can be performed via visual programming or Python scripting. Many analyses are feasible through its visual programming interface(drag and drop connected with widgets)and many visual tools tend to be supported such as bar charts, scatterplots, trees, dendrograms, and heat maps. A substantial amount of widgets(more than 100) tend to be supported.The instrument has machine learning components, add-ons for bioinformatics and text mining, and it is packed with features for data analytics. This is also used as a python library. Python scripts can keep running in a terminal window, an integrated environment like PyCharmand PythonWin, pr shells like iPython. Orange comprises of canvas interface onto which the user places widgets and creates a data analysis workflow. The widget proposes fundamental operations, For example, reading the data, showing a data table, selecting features, training predictors, comparing learning algorithms, visualizing data elements, etc. Orange operates on Windows, Mac OS X, and a variety of Linux operating systems. Orange comes with multiple regression and classification algorithms.Orange can read documents in native and other data formats. Orange is dedicated to machine learning techniques for classification or supervised data mining. There are two types of objects used in classification: learner and classifiers. Learners consider class-leveled data and return a classifier. Regression methods are very similar to classification in Orange, and both are designed for supervised data mining and require class-level data. The learning of ensembles combines the predictions of individual models for precision gain. The model can either come from different training data or use different learners on the same sets of data.Learners can also be diversified by altering their parameter sets. In orange, ensembles are simply wrappers around learners. They act like any other learner. Based on the data, they return models that can predict the results of any data instance.2. SAS Data Mining: SAS stands for Statistical Analysis System. It is a product of the SAS Institute created for analytics and data management. SAS can mine data, change it, manage information from various sources, and analyze statistics. It offers a graphical UI for non-technical users.SAS data miner allows users to analyze big data and provide accurate insight for timely decision-making purposes. SAS has distributed memory processing architecture that is highly scalable. It is suitable for data mining, optimization, and text mining purposes.3. DataMelt Data Mining: DataMelt is a computation and visualization environment which offers an interactive structure for data analysis and visualization. It is primarily designed for students, engineers, and scientists. It is also known as DMelt.DMelt is a multi-platform utility written in JAVA. It can run on any operating system which is compatible with JVM (Java Virtual Machine). It consists of Science and mathematics libraries.Scientific libraries:|
Scientific libraries are used for drawing the 2D/3D plots.Mathematical libraries:
Mathematical libraries are used for random number generation, algorithms, curve fitting, etc.DMelt can be used for the analysis of the large volume of data, data mining, and statistical analysis. It is extensively used in natural sciences, financial markets, and engineering.4. Rattle: Ratte is a data mining tool based on GUI. It uses the R stats programming language. Rattle exposes the statical power of R by offering significant data mining features. While rattle has a comprehensive and well-developed user interface, It has an integrated log code tab that produces duplicate code for any GUI operation.The data set produced by Rattle can be viewed and edited. Rattle gives the other facility to review the code, use it for many purposes, and extend the code without any restriction.5. Rapid Miner: Rapid Miner is one of the most popular predictive analysis systems created by the company with the same name as the Rapid Miner. It is written in JAVA programming language. It offers an integrated environment for text mining, deep learning, machine learning, and predictive analysis.The instrument can be used for a wide range of applications, including company applications, commercial applications, research, education, training, application development, machine learning.Rapid Miner provides the server on-site as well as in public or private cloud infrastructure. It has a client/server model as its base. A rapid miner comes with template-based frameworks that enable fast delivery with few errors(which are commonly expected in the manual coding writing process)
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