Overview of Data Mining its Algorithms, Techniques and applications

Data Mining

  • Introduction of Data Mining

Data Mining: The diversity and proximity of micro-sensors, digital processing, and large-scale storage units in small batches have shown the potential to have a large amount of data in relation to systems during their operation. The larger size and higher complexity of the generated data sets are known as “big data.

” The computer science field of “data mining” has risen to gain useful insights into that information. Data mining is a technique used to obtain relevant information from large data sets or databases. However, the history of data mining goes back to the mid-nineties. It includes mathematical features, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. They are all related to specific areas of data analysis, which is why they are very similar but each has its own drawbacks and solutions.

The basic benefit of data mining is automatically extracting useful resources or information into more information. The purpose of developing computer programs that can be useful in specific contexts and to gain insights into their knowledge has undermined researchers from various fields such as computer science, engineering, mathematics, physics, neuroscience, and cognitive science.

Data mining is the backbone of the Knowledge Discovery in Databases (KDD) process and consists of problem definition, discovery, and data analysis (pre-processing), anonymous pattern detection and data visualization optimization. Currently, this approach is becoming a practice in computer science, medicine, economics, biology, management, environmental science, agricultural science and sports. The development of Information Technology has produced a huge amount of data and big data in various fields. Research in data science and information technology has opened the way to the storage and use of this valuable information for decision-making.

  • Overview of Data Mining

Data mining is the process of extracting useful information and patterns in big data. It is also called the process of retrieving information, mining information from data, extracting information or analyzing data / pattern. Data mining is a logical process used to search large amounts of data to find useful information. The purpose of this method is to find patterns that were previously unknown. Once these patterns have been identified they can continue to be used to make specific decisions to improve their businesses. There are three steps involved in this purpose to check, identify, and submit.

Evaluation: In the first step of data testing here the data is cleaned and converted to another form with significant flexibility and then the problem-based data environment is determined. Pattern Identification: When the data is already analyzed, it is refined and defined in a specific step that is the second step to create a pattern identification. Identify and select patterns that make the best predictions. Deployment: third and the last step in this process is a deployment where Patterns are deployed for the desired outcome.

DATA mining

3. Data Mining Algorithms and Techniques

Classification, Clustering, Regression, Artificial Intelligence, Neural Networks, Organizational Rules, Decision Trees, Genetic Algorithm, Near Assistant Method, etc., are used to derive information from databases.

(I). Classification

Classification is a widely used method of data mining, which uses a set of pre-set examples to develop a model that can set the value of records significantly. Fraud detection and credit risk applications are appropriate for this type of analysis. This method always uses a decision tree or neural segmentation algorithm.

The data segmentation process involves learning and segmentation. Learning training data is analyzed by a classification algorithm. The categorical test data is used to measure the accuracy of the classification rules. If accuracy is accepted the rules can be applied to new data topologies. For a fraudulent request, this will include complete records of fraudulent and effective materials determined on the basis of the record. The graduate learning algorithm uses these predefined examples to find the set of parameters required for proper discrimination. The algorithm then integrates these components into a model called Classifier.

(II). Clustering

Consolidation can be said as a reference to similar categories of objects. By using clustering techniques we can also identify the overlapping regions and gaps in the space and can find patterns for the overall distribution and the relationship between the data features. The classification method can also work in practical ways of separating groups or classes of an item but it can be costly so integration can be used as an overview of inventory selection and classification. For example, to build a customer group based on purchase patterns, to differentiate types with the same functionality.

(III). Predication

The regression technique can be adapted for predication. The regression analysis can be used to show the relationship between single independent variables and dependent variables. In data mining, independent variables are variables that are already known for response and variables are what we want to predict. Unfortunately, many real-world problems are not just a miracle.

For example, sales volumes, stock prices, and product failure rates are all very difficult to predict because they can be subject to complex connections of many different variables. Therefore, more sophisticated techniques (e.g., regression, decision trees, or neural nets) may be needed to predict future values. Similar model types are commonly used in both conceptual and classification. For example, the CART (Classified and Regression T Tree) algorithm tree decision can be used to create both classification trees (categorizing phase variables) and compression trees (to predict continuous response variables). Neural networks can also create both models of fragmentation and transcription.

(IV). Association rule

Integration and coordination often result in the acquisition of constant object set-ups between large data sets. This type of acquisition helps businesses make certain decisions, such as catalog design, cross-selling, and customer behavior analysis. Association Rule algorithms need to be able to generate rules with less than one confidence interval. However, the number of possible Rules of Organization for a given data is usually too large and the maximum portion of the rules is usually of small value (if any).

(V). Neural networks

The neural network is a set of connected / connecting units that are connected and each connection has its own gift. During the learning phase, the network learns by adjusting the instruments so that it can guess the appropriate class labels for input.

Neural networks have an amazing ability to retrieve information from complex or confidential data and can be used to extract patterns and discover patterns that are more complex to detect by humans or other computer systems. This is best suited for both input and input switches. For example, handwritten punctuation, computer training to print English text and many real-world business problems have been used successfully in many industries. Neural networks are great for identifying patterns or trends of data and are well suited for prediction or forecasting requirements.

4. Data Mining Applications

Data mining is a new technology. Besides this, many industries are already using it. Some of these organizations include shopping malls, hospitals, banks, and insurance companies. Most of these organizations combine data mining with things like statistics, pattern recognition and other important tools. Data mining can be used to find patterns and connections that will be difficult to find. This technology is popular with many businesses because it allows them to learn more about their customers and make smarter marketing decisions. Here’s a look at business problems and solutions found using mining technology.

(I). Data mining applied in Social and Human Science

Data mining is used in the social and human sciences for example; Memon developed a system for resolving conflicts during the production of complex products in a rapid manner. Product information is stored in a text format in the database, where valuable information will be extracted through data mining operations/strategies. In a similar vein to the Menon study, Ozturk evaluated the use of data mining with a return tree to estimate the waiting time for manufactured products. For this purpose, training data were used in the predefined measurement models.

Also, in the area of ​​marketing, Hsu and Chen have used data mining combinations to separate customer information and marketing lists. The compatibility of this function comprises the feature of the use of real, mixed, dynamic and large-scale data integration. The proposed algorithm, called CAVE, used the input of information from hierarchical distances and means of variance and entropy.

In another social science perspective, considering social commentary on Networking, Jin proposes LikeMiner. The authors analyzed social work preference using a mining algorithm to measure users’ interests, representations, and the influence of media. The proposed work shows the efficiency of using the system in large-scale Facebook data.

Data mining is being used in social and human sciences for example, Memon developed a  system to solve conflicts during the development of complex products in a quick way. The product information is stored in a textual way in the database, from which valuable information will be extracted through data mining tasks/techniques.  In the  same line of  Menon research,  Ozturk explored the use of data mining with a regression tree to estimate the waiting time of manufactured products.  For this purpose,  training data were used from pre-defined simulation models.·Exploration

·Pattern identification

·Deployment

(II). Data mining applied on Geosciences

Data mining has also been a catalyst for solving geosciences. Other studies have continued to use the process once different techniques have been developed for manipulating petrophysical, geological and seismic data, to assist in the relationship between prediction and identification of oil and minerals, even in areas with natural reservation. Other applications, such as assistance in decision making on critical systems, were also helpful in implementing the plan.

(III). Data Mining applied on Medicine

The work uses this approach to diagnostic and classification analysis, in order to find the rules in a medical database. Statistical methods, such as bootstrap, were used in the training data. Chen has built a customer-based organizational diagnostic model, called ‘PARA’ (basic diagnostics, advanced testing, review, and action), showing the stages and relationships between these and customers in the customer database by mining data.

(IV). Data Mining applied in Environmental Science

Based on the work of Mehendi and Moussaoui and others mentioned, the relentless growth of big data motivated the development of conservation techniques, such as data. Much of this information is intended to assist in making future decisions to solve many problems, such as land and environmental disturbances.

With the increase of information, there was also an increase in the need for input parameters, which prevented the use of standard mathematical models and encouraged the use of machine learning and data mining techniques in these specific situations.

Moreno-Sáez and Mora-López Saez (2014) proposed a scheme for optimizing the distribution of solar irradiance spectra by combining k-methods with data mining. This has been used to group and determine the most suitable parameters and maximum sunlight to reflect any visual effects, from which the retrieval mechanisms and neural networks perform simulations based on moisture content and temperatures. Thus, the main contribution of this work lies in the ability to predict climate systems with specific known and existing parameters.

(V). Data mining applied in Engineering

A framework for automation based mining, which is the domain of various operational databases, was proposed by Xiao and Fan (2014). The framework consists of a phase group for the identification of energy use patterns in buildings and other organizational rules adopted to determine the relationship between energy use and priorities for each group. A major impact of this study has been the use of the framework to improve the functionality of the building, or it can be improved when looking at other building components to achieve better performance.

5. Conclusion

From the given facts, it can be said that the purpose of data mining is to obtain meaningful and structured connections from previously collected data. Many different sites are using data mining as a means to achieve effective use of internal information. Data mining is becoming more widespread in the private and public sectors. Industries such as banking, insurance, pharmaceuticals, and general reinsurance use data mining to reduce costs, improve research, and boost sales. In the public sector, data mining was initially used as a means to detect fraud and pollution, but it has also grown to serve purposes such as measuring and improving program performance.

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6 Comments
  1. Very nice., exploitation of words are appreciable.

  2. Umair is a excellent researcher

  3. Its really informative and helpfull ..

  4. Good work.

  5. Excellent work

  6. Nice blog.

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