Until a couple of decade back, businesses needed on-field surveys to seek the reviews. This approach of surveying the target audience was both costly and time-consuming. The collection of data was intensely challenging due to the lack of population density and diverse demography. However, in the presence of contemporary technologies, it is unwise to rely on the traditional methods of determining consumer demands and behavior. The evolution of technologies over the time allows enterprises to find the trends online inside the comfort of their workplace by utilizing the data mining process model.
By using data mining approach, the companies discover buying and demand trends by applying the machine learning algorithms on online data of consumers. The strategy is successful to such a degree that companies have designated data science and business intelligence departments.
Following data mining prerequisites are essential to know:
Most recent and significant is the case of Facebook. The data of users was compromised as the social network shared the data with Cambridge Analytica – an analytics company. Besides, this data was allegedly used to manipulate the voters’ minds in favor of one party in the US presidential elections. Consequently, Facebook had to pay a massive fine besides facing public outcry.
2. Validity and Integrity of Data
The effectiveness of algorithms depends on the validity of data. If a dataset does not pertain to the target audience, the outcome will be unwanted. Moreover, the age of data is significant too.
For instance, the consumer choices five years back vary from those today. The significance of reliability of source has made gains in recent years owing to the increasing amount of fake news published every day. Thus, scientists need to ensure that the sources are relevant, fresh and reliable.
3. Selection of Appropriate Algorithms
Data Mining Techniques require the application of sophisticated machine learning algorithms to synthesize, integrate and transform data in the form which is favorable for mining. Following are the categories of machine learning algorithms.
- Supervised Machine Learning
- Unsupervised Machine Learning
- Reinforcement Learning algorithms
It requires the input to place bias by human operators. This bias ensures that the outcome does not exceed certain boundaries. Primarily, Classification and Regression, for the identification of elements and patterns respectively, are the two techniques used for such mining.
It involves the set of algorithms where the data scientists are uncertain about the boundaries of results. Thus, they allow the data mining software to decide about the patterns, regardless of how diverse they may be. Clustering and association define the two major groups of algorithms which perform the same tasks as supervised algorithms but without any bias.
These algorithms are based on a reward system. This system ranks an approach higher if it optimizes the performance. Likewise, it reinforces the algorithm to apply on another dataset if the results are not good enough. The reinforcement learning techniques – as google’s TensorFlow – enable the data scientists to develop intelligent neural networks to imitate the human brain.
The preprocessing involves the synthesis of data. Without cleaning and filtration processes, the mining will provide erroneous results. Cleaning ensures that the extraction is carried out on only the relevant, reliable and latest datasets. Moreover, Transformation makes the data more readable for the mining algorithms.
For instance, if the data has a broad range, it is plausible to convert the values into manageable equivalents. This transformation process is performed again once the mining is done to turn the data back into its original form.
Once the data scientists ensure these prerequisites, the data mining processes begin.
Data mining process model involves following essential stages.
1. Data Extraction
This is the most critical stage which involves pattern recognition and element classification. The scientists apply the most suitable algorithm to find useful information from a variety of cleansed datasets.
The problem might require determining if an image has a person standing in it or not. This is a classification and clustering problem where the outcome will comprise of two groups – Images with person and those without person. Similarly, the classification algorithms can identify elements like animals and birds, heavy and small vehicles, and people belonging to different ethnicity apart from many other examples.
The problem of regression and association provide the solution pattern recognition. Such algorithms apply the Machine learning and Deep learning algorithms to find out the trends by mining the datasets — for example, the growth rate of product and services among consumers, the reasons behind growing number of offenses or the trends of telecommunication company users.
2. Pattern Assessment
Once the scientists begin to get the results, they need to find the causes and effects of patterns. If the pattern or recognition fails to produce any useful results, the data mining application will be essentially unsuccessful. To ensure success, the outcome should correspond to a hypothesis and allow the company to make decisions based on the results.
3. Data Visualization
After validation of results and transformation of data back into the original form, the scientists visualize it in the shape of charts, tables, and graphs. The visualization should clearly define the outcome with understandable images. Besides, there should be a balance between the graphics and text to make the visualization more effective. This stage should eliminate ambiguities and allow the executives to make confident decisions.
The text presents a brief data mining introduction, its prerequisites, processes, and applications. It is a vast field with a range of models and applications. The mining algorithms are revolutionizing the mobile app development industry by incorporating AI. Businesses are flourishing at a cosmic pace with the utilization of data mining. The significance of data science reflects in the fact that data scientists are the most in-demand employees.
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