Big Data Mining Techniques and Tools
In today’s digital world, big data has become a crucial aspect for every dynamic business to survive and adapt to ever-changing consumer needs. Big data refers to large and complex data that swamp organizations daily. This data can be structured, semi-structured, or unstructured depending on the source. Businesses are having a hard time deriving valuable insights, as big data usage is growing at exponential rates. These enormous datasets need to be mined to facilitate analysis and subsequent decision-making processes.
Data mining techniques can be classified by different criteria. It could be based on the data sources mined, the database, the kind of information required, or the type of method employed. As big data is being generated at increasing velocity and veracity, and in high volumes, suitable tools that can extract useful information from this data should be chosen. The process of data mining comprises of initial phase of understanding and defining the data for the problem, data preprocessing, data transformation, followed by data modeling and Analysis.
Data Preprocessing
Once the required data based on type and source (target data) is obtained from the selection process, data preprocessing is done to convert raw data into an understandable format that can be transformed further and used for analysis. Data preprocessing involves cleaning, integration, and reduction.
Data cleaning comprises eliminating bad data, filling in missing values, and correcting inconsistencies thereby, ensuring the data is cleansed and produced in a format suitable for extraction.
Data Integration process combines data from different sources of varied formats to form a single unified dataset.
Data Reduction is reducing the size of massive datasets to fit certain use cases.
Let us look into some tools utilized for data preprocessing.
Apache Hadoop tool is a big data framework that allows the distribution of large data processing across various connected computers. It can scale up from a single server to thousands of different machines.
Cloudera is a platform that provides a data storage and processing platform based on Apache Hadoop, and its own data management system for deployment and operations.
Hortonworks Data Flow is an open-source framework that can help process large amounts of data.
Google BigQuery is a highly scalable, serverless data warehouse with a built-in query engine.
Apache Storm is a fast and efficient real-time distributive processing system, that offers computational abilities supporting clusters or machines.
Qubole is a data processing tool that offers a single platform for every use case and is self-optimized for cloud and open-source engines.
Data Transformation/Manipulation
Transformation of data into a format that facilitates the extraction of useful information may vary depending on the business problem at hand. Classification analysis, Clustering, or Outlier detection could be conducted with suitable tools to manifest this process.
Classification is a type of supervised learning where you apply algorithms to decide how new data should be classified. Different classification techniques exist to categorize data into classes. The most common ones include Decision trees, Support Vector Machines, Fuzzy logic, and Bayesian classifiers. A classic example of classification analysis would be classifying customers based on their eligibility for a personal loan. Decision trees can be used to determine if a customer can be given a bank loan or not based on their income, criminal record, and other related details.
Clustering is a data mining technique to identify similar data. It is an example of an unsupervised learning approach and unlike classification, which analyzes class-labeled data objects, clustering analyzes data objects without an identified class label. K-means clustering, hierarchical clustering, and Gaussian mixture models are some of the popular clustering techniques. Streaming services perform cluster analysis to identify high-usage and low-usage viewers so that they can do targeted advertising. Clustering is also widely used in customer profiling, email marketing, medical diagnosis, the insurance sector, and other such applications.
Outlier Detection is identifying data objects called outliers that do not comply with the general expected behavior or model of the data. These outliers are removed from the dataset to discover results with an increased accuracy. Outlier detection techniques can be used extensively in cyber security to identify malicious behavior like password theft or phishing. This technique can also be used in a variety of domains, such as system health monitoring, event detection in sensor networks, and detecting disturbance in the ecosystem.
Some of the tools used to facilitate the data manipulation process are listed below:
Apache Mahout is built in JavaScript on top of the Apache Hadoop framework and is known for recommender engines, clustering, and classification applications.
Tableau is a data manipulation tool developed in Salesforce to connect with any database. It is mainly used in the Business Intelligence industry, and raw data is simplified easily to any format understandable by the users.
RapidMiner provides a drag-and-drop facility that helps non-experts develop workflows without the need for explicit programming.
KNIME integrates various components for machine learning and data mining through its modular data pipelining concept. It is a scalable platform that can process and transform diverse data types and advanced algorithms.
TensorFlow is one of the most popular open-source libraries for numerical computations using data flow graphs. It can facilitate data transformation for different sequence models.
Data Modeling and analysis
The results of the data mining process can be accurate if the final data modeling and analysis phase is done effectively. Once the data is processed and converted to the desired format, meaningful and profitable insights can be gained through methods like Prediction and Regression.
Prediction is used by data miners for analyzing past instances to forecast future events. It is a two-step process, similar to that of data classification. The value for predicted attributes is generated consistently and modeled. Prediction techniques can help analyze trends, establish correlations, and do pattern matching. Prediction can be for the occurrence of an event shortly such as the failure of machinery or a fault in an industrial component, or in the distant future like company profits crossing a certain threshold.
Regression, primarily a form of planning and modeling is used to define the probability of a specific variable. Regression is a statistical modeling technique using previous observations to predict new data values. A classic example of regression is predicting the price of a house (dependent variable) based on its size, rooms, and location (independent variables). Regression techniques are also used for applications ranging from satellite image analysis, and estimation of crop yield to modeling drug responses and financial predictions.
Association finds a hidden pattern in the data set and also the concurrence of different variables that appear very frequently in the dataset. Association rule mining has several applications and is commonly used to help sales correlations in data or medical data sets. Association rules are also widely used for examining and forecasting customer behavior. It calculates a percentage of items customers purchase together.
Data modeling and analysis include the utilization of advanced tools to find previously unknown, valid patterns and relationships in data sets. Some of the popular ones are listed below.
Oracle Data Mining tool creates predictive models and comprises multiple algorithms essential for machine learning tasks. It can be used to build predictive and descriptive data mining applications and to include additional capabilities to existing ones.
SAS Enterprise Miner can mine data, change it, manage information from various sources, and analyze statistics. It offers a graphical UI for non-technical users and allows them to find patterns, model complex relationships, and identify exceptions.
IBM SPSS Modeler speeds up the operational tasks and helps to visualize processed data better. It is used to build predictive models through the interface’s drag-and-drop functionality.
Weka is an open-source ML tool written in JavaScript and can be accessed through a GUI, standard terminal applications, or a Java API. It allows users to build models crucial for testing ideas without writing code.
Erwin is a Data Modeling Tool for creating logical, physical, and conceptual data models. It allows business and technical users to collaborate and maintain models from a central location.
Conclusion
Data mining is a powerful tool that offers many benefits across a wide range of industries. It can help analyze different data from different perspectives. It is a fact that data mining empowers organizations by assisting them in providing profitable outcomes. These outcomes are a result of strategic planning making organizations gain a competitive advantage, leading to improved marketing campaigns and increased efficiency. To achieve these outcomes, it is important to process the data, transform it, build the appropriate model, and evaluate the desired results.
Big data mining and analytics have taken businesses by storm in the past decade. According to a survey by reports and data, the global data mining tools market size was $1.41B in 2022 and is expected to reach a value of $6.35B in 2032. Big data mining harnesses the power of analytics to give organizations a bird’s eye view of evolving market trends and continues to consistently help organizations stay ahead in the global market. To cater to a volatile consumer-inclined market, data scientists, analysts, and researchers must employ effective tools and techniques to increase revenue and customer satisfaction.
