DATA MINING
Data mining is a powerful technique for extracting valuable insights from large datasets, often referred to as Knowledge Discovery in Databases (KDD). The KDD process encompasses several key steps: Data cleaning, Data integration, Data selection, Data transformation, Data mining, Pattern evaluation, and Knowledge presentation.
what is it:
Data mining is the process of extracting information from large datasets to identify patterns, trends, and valuable insights that enable businesses to make data-driven decisions.
Data mining involves automatically searching large datasets to uncover trends and patterns that extend beyond basic analysis. It employs complex mathematical algorithms to analyze data segments and assess the likelihood of future events. Data mining is also known as Knowledge Discovery in Databases (KDD).
Data mining is akin to data science performed by an individual, focused on a specific situation and dataset with a clear objective. This process encompasses various services, including text mining, web mining, audio and video mining, image mining, and social media mining. It is conducted using either simple or highly specialized software. Outsourcing data mining can accelerate the process and reduce operational costs, as specialized firms can leverage advanced technologies to gather data that is difficult to locate manually. Although vast amounts of information are available across various platforms, extracting actionable knowledge remains a significant challenge. The key is analyzing the data effectively to derive meaningful insights that can address problems or drive business development. Numerous powerful tools and techniques are available to mine data and uncover valuable insights.
A relational database is a structured collection of data organized into tables, records, and columns. This organization allows data to be accessed in multiple ways without needing to understand the underlying table structure. Tables within the database facilitate the sharing and retrieval of information, enhancing data searchability, reporting, and overall organization.
TYPES OF DATA MINING
RELATIONAL DATABASE:
A relational database consists of multiple datasets organized into tables, records, and columns. This structure allows for data access in various ways without needing to directly reference the underlying tables. Tables enable effective data search, reporting, and organization.
DATA WAREHOUSES:
A data warehouse is a technology that aggregates data from various sources within an organization to deliver valuable business insights. It collects large volumes of data from areas like Marketing and Finance, which is then used for analytical purposes and informed decision-making. Unlike systems designed for transaction processing, a data warehouse is optimized for data analysis.
DATA REPOSITORIES:
OBJECT-RELATIONAL DATABASE:
The object-relational model combines elements of both object-oriented and relational database models. It supports concepts such as classes, objects, and inheritance. Its main goal is to bridge the gap between relational databases and object-oriented practices commonly used in programming languages like C++, Java, and C#.
TRANSACTIONAL DATABASE:
A transactional database refers to a database management system (DBMS) that can roll back transactions if they are not executed correctly. While this was once a distinctive feature, most modern relational database systems now support transactional capabilities.
