# Deep Learning in Data Mining Management of Industrial and Commercial Enterprises

Authors: Junnan Yi
Journal: Mobile Information Systems
 (2022)

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## Abstract

Due to the frequent use of the Internet and the generation of massive amounts of data, people have entered the era of big data. A large amount of data is generated in people’s daily life. Because these data contain a large amount of information and more complicated content, the process of data analysis and utilization must account for an increasing number of issues. As the problem becomes more complex, the use of deep learning in data mining becomes increasingly important. This article gives a certain introduction and understanding of deep learning and data mining and analyzes and summarizes the application of deep learning in data mining. It introduces a data mining approach for industrial and commercial companies, as well as the requirements of the classic CRISP-DM data mining method. It also exhibits the application of deep learning in industrial time-series data classification in the content of industrial and commercial companies.


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## Body

## 1. Introduction

We cannot do without data analysis and processing in the age of big data. Big data is becoming more common: it is being used to predict diseases, predict rewards, support intelligent transportation, assist corporations in making business decisions, and study customer psychology. As people’s requirements for data results increase, more reliable solutions such as classification, clustering, and neural networks have been proposed. A big amount of data, on the other hand, contains both useful and useless material. In today’s data mining, figuring out how to extract the essence from laborious huge data while removing the dross has become a pressing issue. In response to this demand, people have carried out research and technological advancements on the application of deep learning in data processing to adapt to the application of data in various fields in the era of big data. Time-series data refers to a series of data points arranged or numbered in chronological order. Normally, these data points are selected at equal time intervals. The classification of time-series data is one of the most important problems in data mining [1]. A massive amount of data has been generated as a result of the requirement for industrial equipment status monitoring, equipment health management, and the development of sensor technologies. Discovering valuable information or laws from the obtained data and using this information and laws for analysis and prediction are the task and significance of data mining with time-series data [2, 3].The rest of the paper is organized as follows: Section2 gives data mining, principles of deep learning algorithms are given in Section 3, research and application of deep learning in big data mining in industrial and commercial enterprises are given in Section 4, and the conclusion is discussed in Section 5.


## 2. Data Mining

The process of mining information from massive databases is called data mining [4]. To put it another way, data mining is the nontrivial process of extracting hidden and previously undiscovered information of research value from a huge database of data. Data mining is a commercial process that identifies and analyzes massive amounts of data to find significant patterns and rules [5]. Data mining is a decision support process based on machine learning, artificial intelligence, pattern recognition, etc., to intelligently analyze corporate data, make inductive predictions, and help decision-makers make correct decisions based on the potential patterns that have been mined; divide the entire database into different groups and stipulate that the data in the same group is as close to the same as possible, and the differences between different groups are as small as possible; construct a classification function or model (often called a classifier), which is divided according to the attributes and characteristics of things, and the function or model can map the data items in the database to a specified classification category, namely, classification function [3, 6]. This is the clustering of data; association analysis uses association rules and sequence pattern technology to discover the database. Data prediction, as the name suggests, is grasping the law of the data and making appropriate estimates; deviation detection is analyzing a limited number of extreme data to identify its internal reasons [7].


## 3. Principles of Deep Learning Algorithms

Researchers’ continuous research on neural networks has led to deep learning. It can be said that deep learning is an extension of neural networks. Deep learning is often misunderstood as a machine learning model, but in fact, it is a framework and an idea [8].Deep learning is required to learn sophisticated high-level abstraction functions and to tackle associated artificial intelligence tasks such as pattern recognition, data categorization, clustering, and language interpretation, according to research. For some machine learning algorithms, feature extraction and selection will be mentioned.The advantage of a relatively shallow structure neural network is that it can better realize the representation of high-dimensional complex functions; a very important reason to cite deep learning is its high accuracy; it does not need to manually extract features but automatically extract features; it uses hierarchical processing data, and each layer of the neural network can extract different levels of features of the input data [9].


### 3.1. Principle of DBN

DBN consists of multiple RBMs and a layer of backpropagation network (backpropagation network (BPN)) [10]. An RBM is a two-layer model, including a visible layer composed of m neurons and a hidden layer composed of n neurons. The connection weights between the visible and hidden layers are bidirectional, meaning that they are the same in both directions. Because the neurons in the layers are independent of one another, the probability distribution and training complexity are reduced. The neurons are fully connected between layers as shown in Figure 1.Figure 1
DBN and RBM network architecture.DBN training consists of two parts: pretraining and fine-tuning training. Pretraining is used to train each RBM of each layer, and the output of each layer is the input of the next layer. In the fine adjustment stage, the backpropagation algorithm is used to propagate the training error layer by layer, and the weight parameters of the entire DBN are adjusted.In line adjustment training, due to the pretraining of DBN, the parameter weights of each layer in the network are obtained through training. This is different from the random initialization of traditional neural networks. The network training obtained by pretraining has high training efficiency and avoids the traditional neural network training process that is easy to fall into the local optimality question. The input data of DBN needs to convert a two-dimensional information matrix into a one-dimensional vector, so the two-dimensional structure information of the image is not considered in the training process, which has a certain impact on the accuracy of data classification.


### 3.2. CNN Principle

CNN uses data as the driving force for model training, which is a method produced by the continuous development of artificial neural networks. CNN is a multilayer neural network composed of operations such as convolution, pooling (downsampling), full connection, and recognition, as shown in Figure2.Figure 2
Architecture of a CNN.The output of each layer of CNN is used as the input of the next layer. Each layer is composed of neuron nodes. Each neuron’s output is the feature of the neuron node multiplied by the input value of the preceding layer’s feature multiplied by a specified weight plus the bias. After a nonlinear transformation, the value is obtained. The set of neuron output forms a feature map, and the convolution operation is applied to the feature map for feature extraction. Pooling operation is equivalent to downsampling, applying local averaging to feature maps and merging similar features to reduce the amount of data, reduce the sensitivity of feature extraction results to image deformation, and generalize to more general features. The fully connected operation combines multiple sets of signal output after multiple convolution pooling into a set of signals. The recognition calculation adds a layer of the network to classify and recognize signals according to application requirements. The neuron weight sharing in the CNN network reduces the number of network training parameters and reduces the network training complexity. In addition, CNN can be used to process data in multiple arrays, including one-dimensional signals and sequences, two-dimensional images, three-dimensional images, or volume data.


### 3.3. RNN Principle

RNN is also known as a recurrent neural network. The output of the neuron acts on itself at the following point in time thanks to the ring structure in the network, achieving the goal of “memorizing” the output of the previous point in time and affecting the next point in time. Therefore, it is usually used for processing sequence data, and the structure is shown in Figure3. The network depth of the RNN is the length of time. The historical input state is summarized through the main structure W of the neural network so that the hidden layer h contains the existing input and past “memory.” Its advantage is that it can process sequence data, but its disadvantage is that it is easy to proceed with the iteration. The hidden layer is affected by the historical input. The problem of the fading gradient will gradually lessen or even disappear when the utilization decreases or even disappears. Furthermore, RNN cannot learn features.Figure 3
Schematic diagram of the RNN network principle.


## 3.1. Principle of DBN

DBN consists of multiple RBMs and a layer of backpropagation network (backpropagation network (BPN)) [10]. An RBM is a two-layer model, including a visible layer composed of m neurons and a hidden layer composed of n neurons. The connection weights between the visible and hidden layers are bidirectional, meaning that they are the same in both directions. Because the neurons in the layers are independent of one another, the probability distribution and training complexity are reduced. The neurons are fully connected between layers as shown in Figure 1.Figure 1
DBN and RBM network architecture.DBN training consists of two parts: pretraining and fine-tuning training. Pretraining is used to train each RBM of each layer, and the output of each layer is the input of the next layer. In the fine adjustment stage, the backpropagation algorithm is used to propagate the training error layer by layer, and the weight parameters of the entire DBN are adjusted.In line adjustment training, due to the pretraining of DBN, the parameter weights of each layer in the network are obtained through training. This is different from the random initialization of traditional neural networks. The network training obtained by pretraining has high training efficiency and avoids the traditional neural network training process that is easy to fall into the local optimality question. The input data of DBN needs to convert a two-dimensional information matrix into a one-dimensional vector, so the two-dimensional structure information of the image is not considered in the training process, which has a certain impact on the accuracy of data classification.


## 3.2. CNN Principle

CNN uses data as the driving force for model training, which is a method produced by the continuous development of artificial neural networks. CNN is a multilayer neural network composed of operations such as convolution, pooling (downsampling), full connection, and recognition, as shown in Figure2.Figure 2
Architecture of a CNN.The output of each layer of CNN is used as the input of the next layer. Each layer is composed of neuron nodes. Each neuron’s output is the feature of the neuron node multiplied by the input value of the preceding layer’s feature multiplied by a specified weight plus the bias. After a nonlinear transformation, the value is obtained. The set of neuron output forms a feature map, and the convolution operation is applied to the feature map for feature extraction. Pooling operation is equivalent to downsampling, applying local averaging to feature maps and merging similar features to reduce the amount of data, reduce the sensitivity of feature extraction results to image deformation, and generalize to more general features. The fully connected operation combines multiple sets of signal output after multiple convolution pooling into a set of signals. The recognition calculation adds a layer of the network to classify and recognize signals according to application requirements. The neuron weight sharing in the CNN network reduces the number of network training parameters and reduces the network training complexity. In addition, CNN can be used to process data in multiple arrays, including one-dimensional signals and sequences, two-dimensional images, three-dimensional images, or volume data.


## 3.3. RNN Principle

RNN is also known as a recurrent neural network. The output of the neuron acts on itself at the following point in time thanks to the ring structure in the network, achieving the goal of “memorizing” the output of the previous point in time and affecting the next point in time. Therefore, it is usually used for processing sequence data, and the structure is shown in Figure3. The network depth of the RNN is the length of time. The historical input state is summarized through the main structure W of the neural network so that the hidden layer h contains the existing input and past “memory.” Its advantage is that it can process sequence data, but its disadvantage is that it is easy to proceed with the iteration. The hidden layer is affected by the historical input. The problem of the fading gradient will gradually lessen or even disappear when the utilization decreases or even disappears. Furthermore, RNN cannot learn features.Figure 3
Schematic diagram of the RNN network principle.


## 4. Research and Application of Deep Learning in Big Data Mining in Industrial and Commercial Enterprises

In this section, we will discuss enterprise data reconstruction, classification, and identification of industrial and commercial enterprise data and enterprise data forecast in detail.


### 4.1. Enterprise Data Reconstruction

The strategic and temporary and continuous location of industrial and commercial data is essential for business and commercial research. In actual practice, due to unforeseen market changes, some data is unavailable or missing, which causes problems for data reconstruction and brings difficulties in product design, mining, and event analysis. The traditional methods of data processing are mainly the use of interpreting methods, such as accurate interpretation and line interpretation, and are widely used in rebuilding seafood data such as Argo and seafood. However, due to the loss of valuable information during interpretation, system errors in data recovery are large. It brings major problems to the old methods of data recovery.In the design process from low resolution to high resolution, in contrast to traditional interpretation methods, some of the current methods rely on a large number of samples and learn the principle of switching from low resolution to high resolution in teaching the sample model. An in-depth study can learn complex types from a large number of data sources and can control the performance of that model. It plays an important role in solving the problem of data reconstruction in industrial and commercial applications. Take the reconstruction of industry and business data as an example. High-resolution CNN (SRCNN) is currently the most common method of reconstructed data. The high number of pixels or number of data processing grids is the same and is used as network input. SRCNN has 3 layers of convolutional filtering. The first layer is the object capture, which reads the foreground representations and accurately captures low-resolution data. The second layer is a nonlinear map, which is rebuilt with nonlinear vector transitions. Resolution data is the third stage of reconstruction. By integrating the form of high-resolution data, the final high-resolution image is generated, as shown in Figure4. Ducournau et al. used high-resolution operational temperatures above sea level and sea ice analysis (OSTIA) data in southern African oceans. Since OS-TIA is available with a combination of measured data, the network uses lower data. SST and high-resolution data OSTIA are used as a gift to study the relationship of the map between the two, thus stopping the mod from low resolution to high resolution. A reasonable increase in the number of filters can improve the learning styles of complex types. Comparisons between the SRCNN and the bicubic interpolation method show that the bicubic interpolation method simplifies the data structure, while the SRCNN can add more information and make the data gradient clearer.Figure 4
Enterprise data reconstruction based on deep learning.


### 4.2. Classification and Identification of Industrial and Commercial Enterprise Data

On the other hand, improved data management in industry and commercial companies delivers more data for search and development firms, which causes issues for intelligent data mining. The process of realizing and viewing business data has become a hot topic in big business data smart mining. Traditional data processing and observation often use visual interpretation, manual separation, or complex removal techniques. However, these traditional methods are difficult to complete data processing and identification on time. Currently, many researchers use methods such as the in-depth study of the placement, observation, and output of remote sensing images.Using the structure and identity of a firm as an example, traditional identification and classification study often uses visual interpretation, a method based on physical geometric factors, and a combined comparison method. However, it is difficult to have both the functionality and accuracy of the algorithm. Although the functionality and reliability of the contour-based algorithm have been improved to some degree, it is still difficult to meet the requirements for rapid data mining. Therefore, it is advantageous to use the CNN method in an in-depth study. First of all, in the data processing process, using the Sony Corporation data provided by France (AVISO-SLA) as a data center, the mesoscale vortex identification method based on the contour lines was used to show the South Atlantic Ocean (65 years) from 1998 to 2012. We take the 81°S-16°S... as an example of a vortex as shown in Figure5 and divided into training data and source data analysis according to 70%, 20%, and 10%. And confirm the source of the data. A 9×9 pixel image is used to produce high-resolution cyclonic vortex images, anticyclonic vortex images, and nonvortex images from AVISO-SLA-based vortex samples and to provide notifications and use them for setting up a training network. Second, various training methods and modes (LeNet, AlexNet, and GoogLeNet) are used to further refine the training parameters in the network to achieve higher accuracy. The identity of the identity is shown in Figure 6.Figure 5
Cyclone vortex (blue) and anticyclonic vortex (rose red) identified based on the SLA contour.Figure 6
The classification and recognition results of deep learning are consistent with the sample data.Table1 is a systematic review of the LeNet, AlexNet, and GoogLeNet modes. Although LeNet has a simpler structure, it is trained from the ground up instead of setting up a good benchmark mod like AlexNet and GoogLeNet, so it can get a better configuration. In terms of time, LeNet training time is 1/3 for AlexNet, and the rest time is for GoogleNet. Therefore, if you are learning a convolutional neural network, considering the timing and outcome as a whole, it may seem that retraining with a small mesoscale vortex dataset is more appropriate than using a hard mode and just fine-tuning mode.Table 1
Classification accuracy comparison of LeNet, AlexNet, and GoogLeNet.
Model nameTraining strategyClassification accuracy (%)LeNetLow-level training97.24AlexNetFine-tuning training96.81GoogLeNetFine-tuning training97.21


### 4.3. Enterprise Data Forecast

The most commonly used methods of estimating data are divided into three categories: transaction information, numerical modes, and numerical predictions. The parameters and cognition of the individual have a significant impact on these approaches, and the sophisticated business data process cannot obtain better results with the most complex formulas and heavy readings.Predict huge business data through in-depth learning, combine the new generation technology with business prediction, and break the old bottle business prediction technology, so as to reduce the degree of cognition and promote the use of technology as an innovation tool. RNN can be used to process time data, and LSTM, as an RNN enhancement product, effectively improves the hidden layer of RNN and can handle different types of time-series data training. Thus, there is huge business data, even Zhang et al. used the LSTM model to predict SST data, and the model has only temporary information and lacks location information. Real business ideas should include spatiotemporal knowledge. Therefore, we must make full use of image recognition to capture location information in in-depth study and sequential memory sequence modes to combine the two to create the appropriate imaging mod. Taking SST data, for example, Yang et al. planned a merger of LSTM with CNN. In design mode, time information was first extracted, and then, a convolutional layer was added to produce location information and experimentation. It was shown that the spatiotemporal sequence mode is better than the pure sequence model which can accurately predict the Chinese business dataset.


## 4.1. Enterprise Data Reconstruction

The strategic and temporary and continuous location of industrial and commercial data is essential for business and commercial research. In actual practice, due to unforeseen market changes, some data is unavailable or missing, which causes problems for data reconstruction and brings difficulties in product design, mining, and event analysis. The traditional methods of data processing are mainly the use of interpreting methods, such as accurate interpretation and line interpretation, and are widely used in rebuilding seafood data such as Argo and seafood. However, due to the loss of valuable information during interpretation, system errors in data recovery are large. It brings major problems to the old methods of data recovery.In the design process from low resolution to high resolution, in contrast to traditional interpretation methods, some of the current methods rely on a large number of samples and learn the principle of switching from low resolution to high resolution in teaching the sample model. An in-depth study can learn complex types from a large number of data sources and can control the performance of that model. It plays an important role in solving the problem of data reconstruction in industrial and commercial applications. Take the reconstruction of industry and business data as an example. High-resolution CNN (SRCNN) is currently the most common method of reconstructed data. The high number of pixels or number of data processing grids is the same and is used as network input. SRCNN has 3 layers of convolutional filtering. The first layer is the object capture, which reads the foreground representations and accurately captures low-resolution data. The second layer is a nonlinear map, which is rebuilt with nonlinear vector transitions. Resolution data is the third stage of reconstruction. By integrating the form of high-resolution data, the final high-resolution image is generated, as shown in Figure4. Ducournau et al. used high-resolution operational temperatures above sea level and sea ice analysis (OSTIA) data in southern African oceans. Since OS-TIA is available with a combination of measured data, the network uses lower data. SST and high-resolution data OSTIA are used as a gift to study the relationship of the map between the two, thus stopping the mod from low resolution to high resolution. A reasonable increase in the number of filters can improve the learning styles of complex types. Comparisons between the SRCNN and the bicubic interpolation method show that the bicubic interpolation method simplifies the data structure, while the SRCNN can add more information and make the data gradient clearer.Figure 4
Enterprise data reconstruction based on deep learning.


## 4.2. Classification and Identification of Industrial and Commercial Enterprise Data

On the other hand, improved data management in industry and commercial companies delivers more data for search and development firms, which causes issues for intelligent data mining. The process of realizing and viewing business data has become a hot topic in big business data smart mining. Traditional data processing and observation often use visual interpretation, manual separation, or complex removal techniques. However, these traditional methods are difficult to complete data processing and identification on time. Currently, many researchers use methods such as the in-depth study of the placement, observation, and output of remote sensing images.Using the structure and identity of a firm as an example, traditional identification and classification study often uses visual interpretation, a method based on physical geometric factors, and a combined comparison method. However, it is difficult to have both the functionality and accuracy of the algorithm. Although the functionality and reliability of the contour-based algorithm have been improved to some degree, it is still difficult to meet the requirements for rapid data mining. Therefore, it is advantageous to use the CNN method in an in-depth study. First of all, in the data processing process, using the Sony Corporation data provided by France (AVISO-SLA) as a data center, the mesoscale vortex identification method based on the contour lines was used to show the South Atlantic Ocean (65 years) from 1998 to 2012. We take the 81°S-16°S... as an example of a vortex as shown in Figure5 and divided into training data and source data analysis according to 70%, 20%, and 10%. And confirm the source of the data. A 9×9 pixel image is used to produce high-resolution cyclonic vortex images, anticyclonic vortex images, and nonvortex images from AVISO-SLA-based vortex samples and to provide notifications and use them for setting up a training network. Second, various training methods and modes (LeNet, AlexNet, and GoogLeNet) are used to further refine the training parameters in the network to achieve higher accuracy. The identity of the identity is shown in Figure 6.Figure 5
Cyclone vortex (blue) and anticyclonic vortex (rose red) identified based on the SLA contour.Figure 6
The classification and recognition results of deep learning are consistent with the sample data.Table1 is a systematic review of the LeNet, AlexNet, and GoogLeNet modes. Although LeNet has a simpler structure, it is trained from the ground up instead of setting up a good benchmark mod like AlexNet and GoogLeNet, so it can get a better configuration. In terms of time, LeNet training time is 1/3 for AlexNet, and the rest time is for GoogleNet. Therefore, if you are learning a convolutional neural network, considering the timing and outcome as a whole, it may seem that retraining with a small mesoscale vortex dataset is more appropriate than using a hard mode and just fine-tuning mode.Table 1
Classification accuracy comparison of LeNet, AlexNet, and GoogLeNet.
Model nameTraining strategyClassification accuracy (%)LeNetLow-level training97.24AlexNetFine-tuning training96.81GoogLeNetFine-tuning training97.21


## 4.3. Enterprise Data Forecast

The most commonly used methods of estimating data are divided into three categories: transaction information, numerical modes, and numerical predictions. The parameters and cognition of the individual have a significant impact on these approaches, and the sophisticated business data process cannot obtain better results with the most complex formulas and heavy readings.Predict huge business data through in-depth learning, combine the new generation technology with business prediction, and break the old bottle business prediction technology, so as to reduce the degree of cognition and promote the use of technology as an innovation tool. RNN can be used to process time data, and LSTM, as an RNN enhancement product, effectively improves the hidden layer of RNN and can handle different types of time-series data training. Thus, there is huge business data, even Zhang et al. used the LSTM model to predict SST data, and the model has only temporary information and lacks location information. Real business ideas should include spatiotemporal knowledge. Therefore, we must make full use of image recognition to capture location information in in-depth study and sequential memory sequence modes to combine the two to create the appropriate imaging mod. Taking SST data, for example, Yang et al. planned a merger of LSTM with CNN. In design mode, time information was first extracted, and then, a convolutional layer was added to produce location information and experimentation. It was shown that the spatiotemporal sequence mode is better than the pure sequence model which can accurately predict the Chinese business dataset.


## 5. Conclusion and Future Work

In-depth study is encouraged and developed based on the development of computer software and hardware and neural network. This article outlines the principles of three important algorithms: DBN, CNN, and RNN. Traditional data extraction methods are frequently confused with electronic items and are unable to adequately explain the data’s hidden information. The real methods of in-depth investigation, including data structure and format, module training methods, and outcome analysis, are presented in detail for specific business applications for data reconstruction, classification and identification, and prediction.In-depth business data mining not only improves data mining capabilities but also breaks down the traditional culture of innovation, reducing corporate risks. The continuation of in-depth research in offshore data mining will depend on improving overall planning, maintaining long-term investment in scientific research, and ensuring the sustainability and reliability of big business data gathering. At the same time, to ensure the preservation of maritime knowledge, rational management and supervisors should be formed at each stage in an in-depth reading system, and the effectiveness of the outcome of in-depth study and the extent to which it is applied should be strengthened. Furthermore, by reducing the learning algorithm’s threshold, users can improve their algorithm expertise and avoid corporate uncertainty due to algorithm faults or abuse and ambiguity of risk prevention and relief alternatives, eventually promoting in-depth analysis of corporate data and the extent to which mines are used. People handling corporate and regulatory events, risk prevention and control, and businesses will be clever and practical in the future, as in-depth learning continues to apply to social life and other fields, public knowledge of the process grows, and in-depth learning-based business services evolve.



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*Source: 6974993-2022-04-30.xml*