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Difference between Data Profiling and Data Mining

Last Updated : 06 May, 2023
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1. Data Mining :
Data mining can be defined as the process of identifying the patterns in a prebuilt database. It extracts aberrant patterns, interconnection between the huge datasets to get the correct outcomes.
Data mining, sometimes known as “Knowledge discovery in databases”. We can say that it is a combination of three scientific disciplines i.e., statistics, artificial intelligence and machine learning.

  • Statistics –
    It deals with statistical datasets by analyzing various collections of data. It helps in industrial, organizational and social issues.
  • Artificial Intelligence –
    It is an important part of data mining. It extracts data from several systems.
  • Machine Learning –
    It utilizes data mining techniques and, with the help of some algorithms, it is used to construct models.

Steps followed by Data Mining :

  1. Exploration –
    It is an initial step in data mining which uses statistical techniques and data visualization to customize the character of dataset and to understand the behavior of the data.
  2. Pattern Identification –
    It means finding some interrelation between the coexisting data with some other data.
  3. Deployment –
    It is a method through which we can merge a machine learning model into an existing environmental production for making better decisions in practical life of business on the basis of that data.

Data Mining Techniques and Algorithms :
On the basis of existing databases, by using various kinds of algorithms and techniques, this task is performed. That is Classification, Clustering, Regression, Artificial Intelligence, Neural Networks, Association Rules, Decision Trees, Genetic algorithms, Nearest Neighbor Method, etc.

  • Classification –
    It is a process of searching a model that describes and distinguishes data classes and concepts and to put them in a specific category.
  • Clustering –
    To analyze the data in more specific way, this method is used. It is sometimes called cluster analysis. It can be said as an unsupervised machine learning process to identify and making groups with similar types of data within a huge dataset.
  • Regression –
    It is basically used to analyze the co-relation between continuous values.
  • Association Rule –
    This involves machine learning models to analyze data for patterns in a database. This helps in catalogue design, cross marketing and customer shopping behavior analysis for better decision-making.
  • Neural Networks –
    It can be said as a series of algorithms that aspire to acknowledge underlying relation between databases by the help of that mimics how the human brain operates.
  • Outer detection –
    This kind of data mining approach focuses on identifying data points in the data collection that do not follow an anticipated pattern or behavior. This method may be applied to a variety of fields, including fraud detection, intrusion detection, and others. Additionally called outlier analysis or outlier mining.
  • Sequential Patterns –
    A data mining method called sequential pattern is designed specifically for analyzing sequential data and identifying sequential patterns. It entails searching through a collection of sequences for interesting subsequences. The significance of a sequence can be determined by its length, frequency of recurrence, and other factors.

2. Data Profiling :
Data profiling is a process of analyzing data from the existing one. To transfer the data from one system to another it uses ETL process (i.e., Extract, Transform and Load).

Data profiling is very crucial in :

  • Data Warehouse and Business Intelligence(DW/BI) Projects –
    By the help of ETL, data profiling can detect data quality errors in sources of data.
  • Data conversion and migration projects –
    These transfer’ data from one platform to other sources so that we can add new features to the technologies and upgrade its performance for the organizations.
  • Source system data quality process –
    The data profiling can highlight data which have some continuous issues and the source of the issues (Ex- Inputs, Errors, Data Corruption).

Data Profiling Techniques :

  • Structure Discovery –
    It helps in analyzing the data whether our data is accordant and formatted correctly by applying mathematical statistics on the data, i.e., ( sum, minimum or maximum).
  • Content Discovery –
    This focuses on the specific content to find out errors like specific rows in a table having problems and in which part of the system the issues are occurring.
  • Relationship Discovery –
    This collects the data and discovers the co-relation between different data elements or within a database.

Steps followed by data profiling :

  1. Search for accurate data for data profiling.
  2. Discover the issues and make them correct regarding data quality in a dataset.
  3. By the help of ETL process, data quality issues can be identified.
  4. With the help of some foreign key relationships, hierarchical structures and some intended business rules, the ETL process can be executed perfectly.

Difference between Data Profiling and Data Mining :




01. Data mining is the process of identifying the patterns in a pre-built database. 1. Data profiling is a process of analyzing data from the existing one.
02. It is also called as KDD that is Knowledge Discovery in Databases. It is also known as data archaeology.
03. The purpose of data mining is to built machine learning techniques for real-time needs. The purpose of data profiling is to provide us accuracy, consistency, uniqueness and error free within a dataset.
04. It extracts data by applying some computer-based methodologies and some algorithm. It extracts from the existing raw dataset.
05. The point of data mining is to dig out the data from the sources to resolve some issues through data analysis. The purpose is to collect accurate data for recognizing the use and quality of that data.
06. It is usually executed on the structured data. It is executed on the structured as well as unstructured data.
07. This involves Classification, Clustering, Regression, Association rule and neural networks to perform tasks. This involves discovery and Analytical Techniques to collect informative summaries related to the data.
08. The applications of data mining involve the customer behavior, credit analysis, fraud detection, business intelligence etc. The applications of data profiling involve targeted advertising, fraud and risk detection, image recognition, delivery logistics etc.
09. Tools used for data mining are Weka, RapidMiner, Orange, KNIME, Sisense, SPSS, SPSS Modeler, Rattle, Data Melt etc. Tools used for data profiling are Atlan, Aggregate Profiler, IBM Infosphere Information Analyzer, Informatica Data Explorer, Melissa Data Profiler, Microsoft Docs etc.   

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