Data Mining Description

Data Mining is the process of collecting information and analyzing it for actionable patterns, which can then be used to develop marketing strategies, new products that fit customers’ wants and needs, and cost-saving strategies. Data mining is looking for hidden, valid, and potentially useful patterns in huge data sets. Data Mining is all about discovering unsuspected/ previously unknown relationships amongst the data.

It is a multi-disciplinary skill that uses machine learning, statistics, AI and database technology. The insights derived via Data Mining can be used for marketing, fraud detection, and scientific discovery, etc. Data mining is also called as Knowledge discovery, Knowledge extraction, data/pattern analysis, information harvesting, etc.

Data Mining

How Data Mining works

Business understanding :

The first step is establishing the goals of the project are and how data mining can help you reach that goal. A plan should be developed at this stage to include timelines, actions, and role assignments.

Data understanding :

Data is collected from all applicable data sources in this step. Data visualization tools are often used in this stage to explore the properties of the data to ensure it will help achieve the business goals.

Data preparation :

Data is then cleansed, and missing data is included to ensure it is ready to be mined. Data processing can take enormous amounts of time depending on the amount of data analyzed and the number of data sources. Therefore, distributed systems are used in modern Database Management Systems (DBMS) to improve the speed of the data mining process rather than burden a single system. They’re also more secure than having all an organization’s data in a single Data Warehouse. It’s important to include failsafe measures in the Data Manipulation stage so data is not permanently lost.

Data Modeling :

Mathematical models are then used to find patterns in the data using sophisticated data tools.

Evaluation :

The findings are evaluated and compared to business objectives to determine if they should be deployed across the organization.

Deployment :

In the final stage, the data mining findings are shared across everyday business operations. An Enterprise Business Intelligence platform can be used to provide a single source of the truth for self-service data discovery.

Data Mining

Data Mining Benefits

Automated Decision-Making :

Data Mining allows organizations to continually analyze data and automate both routine and critical decisions without the delay of human judgment. Banks can instantly detect fraudulent transactions, request verification, and even secure personal information to protect customers against identity theft. Deployed within a firm’s operational algorithms, these models can collect, analyze, and act on data independently to streamline decision making and enhance the daily processes of an organization.

Accurate Prediction and Forecasting :

Planning is a critical process within every organization. Data mining facilitates planning and provides managers with reliable forecasts based on past trends and current conditions. Macy’s implements demand forecasting models to predict the demand for each clothing category at each store and route the appropriate inventory to efficiently meet the market’s needs.

Cost Reduction :

Data mining allows for more efficient use and allocation of resources. Organizations can plan and make automated decisions with accurate forecasts that will result in maximum cost reduction. Delta imbedded RFID chips in passengers checked baggage and deployed data mining models to identify holes in their process and reduce the number of bags mishandled. This process improvement increases passenger satisfaction and decreases the cost of searching for and re-routing lost baggage.

Customer Insights :

Firms deploy data mining models from customer data to uncover key characteristics and differences among their customers. Data mining can be used to create personas and personalize each touchpoint to improve overall customer experience. In 2017, Disney invested over one billion dollars to create and implement “Magic Bands.” These bands have a symbiotic relationship with consumers, working to increase their overall experience at the resort while simultaneously collecting data on their activities for Disney to analyze to further enhance their customer experience.

Data Mining

Types of Data Mining

Data mining has two primary processes: supervised and unsupervised learning.

Supervised Learning :

The goal of supervised learning is prediction or classification. The easiest way to conceptualize this process is to look for a single output variable. A process is considered supervised learning if the goal of the model is to predict the value of an observation. One example is spam filters, which use supervised learning to classify incoming emails as unwanted content and automatically remove these messages from your inbox.

Unsupervised Learning :

Unsupervised tasks focus on understanding and describing data to reveal underlying patterns within it. Recommendation systems employ unsupervised learning to track user patterns and provide them with personalized recommendations to enhance their customer experience.

Data Mining

Data Mining - Supervised Learning

Common analytical models used in supervised data mining approaches are:

Linear Regressions :

Linear regressions predict the value of a continuous variable using one or more independent inputs. Realtors use linear regressions to predict the value of a house based on square footage, bed-to-bath ratio, year built, and zip code.

Logistic Regressions :

Logistic regressions predict the probability of a categorical variable using one or more independent inputs. Banks use logistic regressions to predict the probability that a loan applicant will default based on credit score, household income, age, and other personal factors.

Time Series :

Time series models are forecasting tools which use time as the primary independent variable. Retailers, such as Macy’s, deploy time series models to predict the demand for products as a function of time and use the forecast to accurately plan and stock stores with the required level of inventory.

Classification or Regression Trees :

Classification Trees are a predictive modeling technique that can be used to predict the value of both categorical and continuous target variables. Based on the data, the model will create sets of binary rules to split and group the highest proportion of similar target variables together. Following those rules, the group that a new observation falls into will become its predicted value.

Neural Networks :

A neural network is an analytical model inspired by the structure of the brain, its neurons, and their connections. These models were originally created in 1940s but have just recently gained popularity with statisticians and data scientists. Neural networks use inputs and, based on their magnitude, will “fire” or “not fire” its node based on its threshold requirement. This signal, or lack thereof, is then combined with the other “fired” signals in the hidden layers of the network, where the process repeats itself until an output is created. Since one of the benefits of neural networks is a near-instant output, self-driving cars are deploying these models to accurately and efficiently process data to autonomously make critical decisions.

K-Nearest Neighbor

The K-nearest neighbor method is used to categorize a new observation based on past observations. Unlike the previous methods, k-nearest neighbor is data-driven, not model-driven. This method makes no underlying assumptions about the data nor does it employ complex processes to interpret its inputs. The basic idea of the k-nearest neighbor model is that it classifies new observations by identifying its closest K neighbors and assigning it the majority’s value. Many recommender systems nest this method to identify and classify similar content which will later be pulled by the greater algorithm.

Data Mining

Data Mining - Unsupervised Learning

Common analytical models used in unsupervised data mining approaches are:

Clustering :

Clustering models group similar data together. They are best employed with complex data sets describing a single entity. One example is lookalike modeling, to group similarities between segments, identify clusters, and target new groups who look like an existing group.

Association Analysis :

Association analysis is also known as market basket analysis and is used to identify items that frequently occur together. Supermarkets commonly use this tool to identify paired products and spread them out in the store to encourage customers to pass by more merchandise and increase their purchases.

Principal Component Analysis :

Principal component analysis is used to illustrate hidden correlations between input variables and create new variables, called principal components, which capture the same information contained in the original data, but with less variables. By reducing the number of variables used to convey the same level information, analysts can increase the utility and accuracy of supervised data mining models.

Data Mining Parameters

In data mining, association rules are created by analyzing data for frequent if/then patterns, then using the support and confidence criteria to locate the most important relationships within the data. Support is how frequently the items appear in the database, while confidence is the number of times if/then statements are accurate.

Other data mining parameters include Sequence or Path Analysis, Classification, Clustering and Forecasting. Sequence or Path Analysis parameters look for patterns where one event leads to another later event. A Sequence is an ordered list of sets of items, and it is a common type of data structure found in many databases. A Classification parameter looks for new patterns, and might result in a change in the way the data is organized. Classification algorithms predict variables based on other factors within the database.

Clustering parameters find and visually document groups of facts that were previously unknown. Clustering groups a set of objects and aggregates them based on how similar they are to each other.

There are different ways a user can implement the cluster, which differentiate between each clustering model. Fostering parameters within data mining can discover patterns in data that can lead to reasonable predictions about the future, also known as predictive analysis.

Data Mining and Data Warehousing

Data can be mined whether it is stored in flat files, spreadsheets, database tables, or some other storage format. The important criteria for the data is not the storage format, but its applicability to the problem to be solved.

Proper data cleansing and preparation are very important for data mining, and a data warehouse can facilitate these activities. However, a data warehouse will be of no use if it does not contain the data you need to solve your problem.

Oracle Data Mining requires that the data be presented as a case table in single-record case format. All the data for each record (case) must be contained within a row. Most typically, the case table is a view that presents the data in the required format for mining.

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  • user 1

    Mick Jones

    " I was lucky to find web-parsing web scraping services for my projects as their work is very accurate and professional. It is very difficult to find a company offering all web scraping, screen scraping, web data extraction, Data Mining and Big Data solutions with high end accuracy and on time."

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