APPLICATION AND TRENDS IN DATA MINING

APPLICATION AND TRENDS IN DATA MINING. Ms. Essel Tumaliuan Cañaberal.

Data Mining. i s an interdisciplinary subfield of Computer Science. Data Mining is the Computational Process of discovering Pattern in Large Data Sets involving Method at the intersection of artificial intelligence , machine learning statistics and Database Systems. The Overall goal of the Data Mining process is to extract information from a data set and transform it into an understandable structure for further use. is a process that analyse a large amount of data to find new and hidden information that improves business.

Applicable fields. Data Mining Applications in Sales/Marketing Data Mining Applications in Banking / Finance Data Mining Applications in Healthcare and Insurance Data Mining Applications in Transportation Data Mining Application in Medicine Data Mining Applications in Education Data Mining Applications in Manufacturing Engineering Research analysis.

Data mining enables businesses to understand the hidden patterns inside historical purchasing transaction data. Data mining is used for Market Basket Analysis to provide information on what product combinations were purchased together when they were bought and in what sequence. This information helps businesses promote their most profitable products and maximize the profit. In addition, it encourages customers to purchase related products that they may have been missed or overlooked Data mining helps determine the distribution schedules among warehouses and outlets and analyses loading patterns..

Data Mining Applications in Sales/Marketing. For retailers, data mining can be used to provide information on product sales trends, customer buying habits and preferences, supplier lead times and delivery performance, seasonal variations, customer peak traffic periods, and similar predictive data for making proactive decisions. How is data used in retail? Big in retail enables companies to create customer recommendations based on their purchase history , resulting in personalized shopping experiences and improved customer service. These super-sized data sets also help with forecasting trends and making strategic decisions based on market analysis. WHY IS DATA COLLECTION IMPORTANT? Collecting — and interpreting — data may seem like a long, daunting process. However, it saves you money in the long run, by helping guide your retail strategies to maximize returns. elp you streamline your processes and marketing campaigns in the future — saving you money..

DATA YOU SHOULD BE COLLECTING. Shopper Demographics Shopping Trends Trend Analysis Impact of Promotions Inventory Movement Trends Stock Replenishment Cycles Demand Spikes Periodic Sale Comparison Fast and Slow Moving Products Store-Help Induced Sales Shopper Feedback Order-to-Deliver Time Lag Product Visibility Impact.

HOW TO COLLECT AND ANALYZE YOUR DATA. Data collection, for large amounts of data, occurs in several stages: Collecting data Storing data Organizing data Analyzing data Visualizing data Taking action and producing results.

Marketing Data mining is used to explore increasingly large databases and to improve market segmentation. By analysing the relationships between parameters such as customer age, gender, tastes, etc., it is possible to guess their behaviour in order to direct personalised loyalty campaigns. Data mining in marketing also predicts which users are likely to unsubscribe from a service, what interests them based on their searches, or what a mailing list should include to achieve a higher response rate. Retail Supermarkets, for example, use joint purchasing patterns to identify product associations and decide how to place them in the aisles and on the shelves. Data mining also detects which offers are most valued by customers or increase sales at the checkout queue..

Benefits of Data MIning in Retail / Marketing. Improving Customer Service Real-life data mining examples: Amazon is a is the world’s largest online retailer. Its online marketplace platform leverages big data with a customer-centric approach to improve customer experience and user delights..

Innovation And Product Development. Show you how products can most effectively appeal to customers Show areas of highest likely ROI from innovation Help you monitor and follow the competitor's innovations Help you understand what motivates Wags Ddtd Help You Drive Innovations Can enable scalability among new Help you create environments that support testing of ideas outcomes from different ideas and plans Show you ways to reducing costs for new product development.

Social Media Optimization. Ways Data Help Social Media Identify important social media trends and signals Optimization Help you Show you what optimize your kind of brand's products performance consumers like on social media and dislike Indicate what type of content work the best on social media.

Boosting SEO (Search Engine Optimization) of Your Website.

Defining Profitable Store Locations. Data intelligence has the power to help retailers make better decisions about where to open new stores.Retailers can identify new locations for expansion and work out the sales estimates for these places through deep analysis of socio-economic data..

Marketing And Sales Forecasts. Big data has changed the way businesses sell to customers, which helps companies increase their performance and profits. There are many examples of how companies use data to predict and boost sales. Data analytics allow businesses to predict products that customers may want to purchase, to influence the customer’s behavior, to forecast trends, and to optimize the sales funnel and marketing campaigns..

Market Basket Analysis (MBA). Market Basket Analysis is one of the key data mining techniques widely used by retailers to boost business as predicting what items customers buy together or what goods are placed in the same basket by customers..

Data mining applications in banking / finance. Banks use data mining to better understand market risks. It is most often used in banking to determine the likelihood of a loan being repaid by the borrower. It is also used commonly to detect financial fraud. An example used is fraud detection is when some unusually high transactions occur, and the bank’s fraud prevention system is set up to put the account on hold until the account holder confirms that this was a legitimate purchase..

Loan Payment Prediction Data mining methods like attribute selection and attribute ranking will analyze the customer payment history and select important factors such as payment to income ratio, credit history, the term of the loan, etc. The results will help the banks decide its loan granting policy, and also grant loans to the customers as per factor analysis..

Data Mining Applications in Healthcare and Insurance.

Benefits of data mining in healthcare. 1. Enhanced clinical decision-making These systems either use a knowledge base and apply rules to drive decisions or utilize machine learning to make inferences based on data analysis. Solutions of the latter kind benefit greatly from data mining — for instance, when comparing a patient's history and symptoms with current clinical research or similar cases. 2. Increased diagnosis accuracy The use of data mining in healthcare helps doctors make more conclusive, evidence-based diagnoses in a short time frame. While it still takes an experienced clinician to arrive at the final decision, AI-enabled software can process vast arrays of data in a matter of seconds. 3. Improved treatment efficiency Every healthcare provider strives to achieve the best quality of medical care for its patients. With data mining, analyzing the available treatment plans, comparing their efficacy, and selecting the best one becomes easy.

4. Avoiding harmful drug and food interactions Data mining in healthcare can help mitigate those risks. While the most dangerous drug interactions are well studied , new drugs are being developed constantly, and there's always a chance of human error. Doctors, nurses, and patients will all benefit from a system that can track the chemical composition of medications and analyze research and clinical data. 5.Detection of insurance fraud 6.Enabling predictive analysis.

Data Mining Applications in Education. is the process of raw data transformation from large educational databases to useful and meaningful information which can be used for a better understanding of students and their learning conditions, improving teaching support as well as for decision making in educational systems.

MOOCs and online courses TEACHERS COLLEGE Big Data in Education *IntroVdet NIESTIGATON Classied: The Afgnans,'dl.

Grade and Outcome Data Elementary Middle High School KI 2 34 S 8 9S1 9S2 1M IISI 1152 12Sl 12S2 -2C0 CBOData.

Data in Education Used to Be Dispersed Hard to Collect Small-Scale.

Goals • Joint goal of exploring the "big data" now available on learners and learning • To promote — New scientific discoveries & to advance science of learning — Better assessment of learners along multiple dimensions • Social, cognitive, emotional, meta-cognitive, etc. • Individual, group, institutional, etc. — Better real-time support for learners.

Data Mining Applications in Manufacturing Engineering.

Data Mining in Scientific Applications. Scientific data can be amassed at much higher speeds and lower costs. This has resulted in the accumulation of huge volumes of high-dimensional data, stream data, and heterogeneous data, containing rich spatial and temporal information.Scientific applications are shifting from the “hypothesize-and-test” paradigm toward a “collect and store data, mine for new hypotheses, confirm with data or experimentation” process. Data Warehouses and data preprocessing. Graph-based mining. Visualization and domain specific knowledge..

Data Mining for Intrusion Detection. Modern network technologies require a high level of security controls to ensure safe and trusted communication of information between the user and a client. An intrusion Detection System is to protect the system after the failure of traditional technologies. Data mining is the extraction of appropriate features from a large amount of data. And, it supports various learning algorithms, i.e. supervised and unsupervised. Intrusion detection is basically a data-centric process so, with the help of data mining algorithms, IDS will also learn from past intrusions, and improve performance from experience along with find unusual activities. It helps in exploring the large increase in the database and gather only valid information by improving segmentation and help organizations in real-time plan and save time. It has various applications such as detecting anomalous behavior, detecting fraud and abuse, terrorist activities, and investigating crimes through lie detecti on.

Trends in Data Mining. Exploration of applications: addressing application-specific issues Data mining approaches that are scalable and interactive Data mining integration with Web search engines, database systems, data warehouse systems, and cloud computing systems Mining social and information networks Mining spatiotemporal, moving objects, and cyber-physical systems Mining multimedia, text, and web data Mining biological and biomedical data Visual and audio data mining Distributed data mining and real-time data stream mining..

10 Big Data Trends You Should Know About For 2022.

Data Fabric Vs. Data Mesh “Data fabric” and “data mesh” are emerging architectures for integrating, accessing and managing data across multiple heterogeneous platforms and technologies. But there are differences, so expect to hear more about both in 2022 along with some debate – and possibly some confusion. Data Observability Goes Mainstream Data analytics and data-intensive applications are key components of many digital transformation and machine learning initiatives – making it critical that the quality, reliability and completeness of the data used for those projects meet high standards. Increased Deployment And Large-Scale Use Of Machine Learning Machine learning has been a hot area in the last few years with both established IT vendors and – especially – startups offering software for developing, training, deploying and managing machine learning models and the data they use..

The Rise Of Comprehensive Data Governance Platforms data managers are looking to automate data governance processes have had to rely on point products and tools with specific functionality including data catalogs, data lineage, data quality, data access control, data security, master data management and more. Increased Use Of DataSecOps Technologies As a corollary to the need for better data governance, businesses and organizations will also increasingly turn to DataSecOps software from vendors such as Immuta and Satori to ensure data protection and data privacy policies are being followed Supply Chain Analytics Becomes A Strategic Imperative For many businesses managing their way through supply chain disruptions has been the biggest challenge associated with the COVID-19 pandemic. While the disruptions were triggered by shuttered manufacturing plants around the world, those problems were compounded by the lack of visibility many businesses have into their supplier networks, making it difficult to shift plans, find alternative suppliers, and adjust distribution to match supply with demand..

Expanded Use Of Predictive Analytics To Overcome “The Great Resignation” Data analytics has traditionally been applied to human resource management for basic reporting tasks such as compiling employment data for tax purposes. But some forward-thinking businesses and organizations have begun applying the same kind of predictive technologies used to monitor customer churn to identifying key employees that may be on the verge of quitting by analyzing data around compensation, job satisfaction, productivity and other metrics. Data Marketplace Use Will Explode Business analytics initiatives have traditionally focused on analyzing internally generated data such as sales, market surveys and business performance. But increasingly businesses are obtaining data from external sources and using it to supplement and enrich their own data: IDC says that 75 percent of enterprises in 2021 used external data sources to strengthen cross-functional and decision-making capabilities..