Latest Trends and Data Mining Concepts ,An Overview of Data Mining Techniques and Applications

G-SAS ITBIZ Solutions(Software & Recruitment Compa

6 min readOct 4, 2020

Data has been used from time immemorial by various companies to manage their operations.Data is needed by various organizations strategically aimed at expanding their business operations, reduction of costs, improve their marketing force and above all improve profitability. Data mining is aimed at the creation of information assets and uses them to leverage their objectives.

In this article, we discuss some of the common questions asked about the data mining technology. Some of the questions we have addressed include:

Data Mining Defined :

Data mining can be regarded as a new concept in the enterprise decision support system, usually abbreviated as DSS. It does more than complementing and interlocking with the DSS capabilities that may involve reporting and query. It can also be used in on-line analytical processing (OLAP), traditional statistical analysis and data visualization. The technology comes up with tables, graphs, and reports of the past business history.

We may define data mining as modeling of hidden patterns and discovering data from large volumes of data.It is important to note that data mining is very different from other retrospective technologies because it involves the creation of models. By using this technology, the user can discover patterns and use them to build models without even understanding what you are after. It gives an explanation why the past events happened and even predicting what is likely to happen.

Some of the information technologies that can be linked to data mining include neural networks, fuzzy logic, rule induction and genetic algorithms. In this article, we do not cover those technologies but focus on how data mining can be used to meet your business needs and you can translate the solutions thereafter into dollars.

Setting Your Business Solutions and Profits :-

One of the common questions asked about this technology is; what role can data mining play for my organizations? At the start of this article, we described some of the opportunities that can be associated with the use of data. Some of those benefits include cost reduction, business expansion, sales and marketing, and profitability. In the following paragraphs, we look into some of the situations where companies have used data mining to their advantage.

Data Mining Applications

Here is the list of areas where data mining is widely used −

Financial Data Analysis Retail Industry Telecommunication Industry Biological Data Analysis
Other Scientific Applications
Intrusion Detection

Financial Data Analysis

The financial data in banking and financial industry is generally reliable and of high quality .

Some of the typical cases are as follows -

Design and construction of data warehouses for multidimensional data analysis and data mining.

Loan payment prediction and customer credit policy analysis.

Classification and clustering of customers for targeted marketing.

Detection of money laundering and other financial crimes.

Retail Industry:

Data Mining has its great application in Retail Industry because it collects large amount of data from on sales, customer purchasing history, goods transportation, consumption and services.

Design and Construction of data warehouses based on the benefits of data mining.

Multidimensional analysis of sales, customers, products, time and region.

Analysis of effectiveness of sales campaigns.

Customer Retention.

Telecommunication Industry:

Today the telecommunication industry is one of the most emerging industries providing various services such as fax, pager, cellular phone, internet messenger, images, e-mail, web data transmission, etc.

Here is the list of examples for which data mining improves telecommunication services −

Multidimensional Analysis of Telecommunication data.

Fraudulent pattern analysis.

Identification of unusual patterns.

Multidimensional association and sequential patterns analysis.

Mobile Telecommunication services.

Use of visualization tools in telecommunication data analysis.

Biological Data Analysis:

we have seen a tremendous growth in the field of biology such as genomics, proteomics, functional Genomics and biomedical research.

Following are the aspects in which data mining contributes for biological data analysis −

Alignment, indexing, similarity search and comparative analysis multiple nucleotide sequences.

Discovery of structural patterns and analysis of genetic networks and protein pathways.

Association and path analysis.

Visualization tools in genetic data analysis.

Other Scientific Applications:-

The applications discussed above tend to handle relatively small and homogeneous data sets for which the statistical techniques are appropriate. Huge amount of data have been collected from scientific domains such as geosciences, astronomy, etc.

Data Warehouses and data preprocessing.
Graph-based mining.
Visualization and domain specific knowledge.

Intrusion Detection
Intrusion refers to any kind of action that threatens integrity, confidentiality, or the availability of network resources.

Development of data mining algorithm for intrusion detection.

Association and correlation analysis, aggregation to help select and build discriminating attributes.

Analysis of Stream data.

Distributed data mining.

Visualization and query tools.

Choosing a Data Mining System-

The selection of a data mining system depends on the following features −

Data Types −

The data mining system may handle formatted text, record-based data, and relational data. The data could also be in ASCII text, relational database data or data warehouse data.

System Issues −

One data mining system may run on only one operating system or on several. There are also data mining systems that provide web-based user interfaces and allow XML data as input.

Data Sources −

Data sources refer to the data formats in which data mining system will operate. Some data mining system may work only on ASCII text files while others on multiple relational sources. Data mining system should also support ODBC connections or OLE DB for ODBC connections.

Data Mining functions and methodologies −

There are some data mining systems that provide only one data mining function such as classification while some provides multiple data mining functions such as concept description, discovery-driven OLAP analysis, association mining, linkage analysis, statistical analysis, classification, prediction, clustering, outlier analysis, similarity search, etc.

Coupling data mining with databases or data warehouse systems −

Data mining systems need to be coupled with a database or a data warehouse system.

Here are the types of coupling listed below −

No coupling
Loose Coupling
Semi tight Coupling
Tight Coupling

Scalability −

There are two scalability issues in data mining −

Row (Database size) Scalability − A data mining system is considered as row scalable when the number or rows are enlarged 10 times. It takes no more than 10 times to execute a query.

Column (Dimension) Salability − A data mining system is considered as column scalable if the mining query execution time increases linearly with the number of columns.

Visualization Tools −

Visualization in data mining can be categorized as follows −

Data Visualization
Mining Results Visualization
Mining process visualization
Visual data mining

Data Mining query language and graphical user interface − An easy-to-use graphical user interface is important to promote user-guided, interactive data mining.

Trends in Data Mining
Data mining concepts are still evolving and here are the latest trends that we get to see in this field −

Application Exploration.

Scalable and interactive data mining methods.

Integration of data mining with database systems, data warehouse systems and web database systems.

SStandardization of data mining query language.

Visual data mining.

New methods for mining complex types of data.

Biological data mining.

Data mining and software engineering.

Web mining.