Introduction
In today's world of data, businesses want to get useful information from big data sets. They use two main methods: data mining and data profiling. While Data Mining is the process of discovering patterns and connections in large data sets, Data Profiling is analyzing data characteristics to understand its quality and structure.
In this article, we will be discussing the key features and applications of Data Mining and Data profiling, followed by a table on Data Mining vs Data Profiling.
Understanding Data Mining
Data Mining is the process of discovering trends, patterns, connections, and important information from big sets of structured data. It uses different tools and algorithms to process data and find their patterns, which businesses later use in data-based decision-making.
Powerful Trio of Data Mining
Data Mining is like a blend of three scientific fields: Statistics, Artificial Intelligence, and Machine Learning. While statistics help analyze the datasets, the power of AI is used to get data from different systems, and machine learning is used to build models based on the different data mining methods.
Together the three help to find useful information from large data sets.
Data Mining Process
The Data Mining process is divided into five broad steps:
Define business goals
Set clear goals to be achieved within a definite time period. These goals provide a direction for the organization of the company resources. Business goals can include revenue targets, customer satisfaction, innovation, etc.
Data understanding
Data understanding is an important step in data mining. It is the first step of data mining, where we use statistics to understand and modify the characteristics of the dataset. By understanding the data, data analysts and data scientists can make informed decisions on how to preprocess, clean, and transform the data to ensure it is suitable for further analysis.
Pattern Identification
Pattern identification means finding meaningful relationships between different data points that exist together in the database. This step involves applying different data mining methods, such as clustering, classification, regression, and association rule mining, to get valuable patterns and insights.
Accuracy
Check the degree of correctness of the results obtained from data analysis. High accuracy in data analysis ensures that business decisions are based on reliable information, reducing the risk of making costly mistakes.
Apply findings
It is the process by which the results of the data analysis obtained by the machine learning models are applied in real-life businesses to make better decisions. This involves integrating the data-driven discoveries into real-life business processes, actions, and decision-making frameworks.
Data Mining Methods and Algorithms
Data Mining can be performed using many methods and algorithms, such as:
Classification
It is a process of sorting things into different groups based on their characteristics or features. Common algorithms used for classification include Decision Trees, Random Forests, Support Vector Machines (SVM), Naive Bayes, and k-Nearest Neighbors.
Regression
Regression is a data mining method focused on modelling the relationship between a dependent variable (target) and one or more independent variables (predictors) that change together in a smooth and continuous manner.
Clustering
It is like putting similar things in a big group without knowing the groups beforehand. It helps find patterns in a large dataset by identifying similar data points and grouping them together. This is quite helpful in customer segmentation, error detection, etc.
Neural Networks
It is a set of machine learning algorithms that work like a human brain to understand the connections and relationships between the data. Neural Networks are used for many tasks like image recognition, natural language processing, and pattern classification.
Association rule
Association Rule is a method that uses machine learning models to look for patterns in datasets. It helps in designing catalogues, cross-marketing, and understanding the shopping behaviour of customers to make smart decisions by keeping a track of the co-occurring items in the data set.
Sequential patterns
Sequence patterns are a data mining method used to analyze data that appears in a sequence.
Outer detection
It is the data mining approach that looks at data points that behave differently than the expected pattern. It is generally used to detect fraud.
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