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Knowledge and understanding of big data, data mining and machine learning

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What can data mining and big data do?

In short, they give us predictive power.

Our lives are already digital

Many of the things we do every day can be written down. Every credit card transaction is digital and traceable. Our public image is monitored by many CCTV stations in every corner of the city; for enterprises, most of the financial and operational data is stored in one or another type of ERP; With the advent of wearable devices, every heartbeat and breath are all digitized and stored as useful data. With much of our life being digitized, computers can now “understand” our world better than ever.

2. If the model remains the same, past = future.

Many things in our life show patterns. For example, a person might travel between work and family on any work day, go on vacation, or watch a movie on any non-work day. It is unlikely that this pattern will change. On any given day, the store will have peak and downtime hours, and this is unlikely to change. Companies will require higher labor intensity during certain months of the year, and this model is unlikely to change.

Summarizing points 1 and 2, we can conclude that if past patterns are provided, the computer is more likely to predict the future, because these patterns are likely to be consistent over a long period of time.

If a computer can predict people’s lifestyles, it will know exactly when is the best promotion. For example, if this person washes their car every Friday every Friday or it’s a coupon, then this is a car wash promotion. 

Go on vacation in March, so stay. From a business perspective, the computer can also forecast store sales for the day and then formulate business strategies to maximize total revenue. For enterprises, the computer can also develop the best work plan with the most reasonable distribution of personnel.

Once the future is predictable, we can plan ahead and prepare for the best action at any time. Just like Neo in The Matrix, he can dodge all bullets because he can clearly see the source of the bullets. According to Sherlock Holmes, “a deep knowledge of the mathematics of probability, a deep understanding of human psychology and the known tendencies of any particular person can significantly reduce the number of variables.” In other words, “big data gives us predict the strength of the future.” 

This is the power of data mining. Data mining has always been associated with big data because big data supports a large number of datasets that serve as the basis for all predictions.

So what are big data, data mining and machine learning?

Large amount of data

When the amount of data is huge, it is obvious that this data cannot be processed on any machine. A very large file, say 10 GB, you won’t be able to open it on any Windows system and then it all crashes. Big data has been developed for this. You can think of it as a special kind of software that breaks large files into smaller ones, which can then be processed on multiple computers. The process of splitting and combining chunks of data is called MapReduce. The software environment most commonly used for this process is called Hadoop. Hadoop solves the underlying problems, and there are many tools that can be used with Hadoop, such as Pig, Zookeeper, and Hive, to simplify the process. Hadoop and many of its related tools are often referred to as “big data technology.”

Machine learning

We just touched on how the data was processed. Suppose this piece of data contains a shopping behavior group including the total number of items purchased and the number of items purchased by each customer. 

So far, this is a simple statistical analysis. However, if our goal is to analyze the correlation between different types of shoppers, or if we want to infer the specific preferences of specific types of shoppers, or even predict the gender or age of any shopper, we’ll need more Complex Models, we call them algorithms. Machine learning is easier to understand how all the different types of algorithms designed for data mining purposes such as logistic regression, decision trees, collaborative filtering, etc.

Data collection

By applying machine learning algorithms, existing data can be used to predict unknowns. 

This is why the miracle of data mining is closely related to machine learning. However, the strength of any machine learning algorithm relies heavily on having large datasets. Remember, no matter how complex an algorithm is, you cannot make inspiring predictions from multiple lines of data. Big data technology is a prerequisite for machine learning. Using machine learning, we can gain valuable insights from existing datasets. This is data mining.

Data mining smart card

The scientific research area of ​​data mining is relatively traditional, mainly data processing, data mining and valuation models. With the advancement of the Internet and Big Data, the applications of data mining are becoming wider and wider. We don’t have to stick to the boundaries and differences of data mining, data analysis, and machine learning. Perhaps these boundaries and differences are set artificially. After all, whatever technology is, what can solve practical problems is good technology.

Why is it so good?

Data mining is a method of extracting hidden, unknown, potential and useful information and knowledge from huge amounts of data. This information can have potential value, be interesting to users, understandable and useful, inform decision-making, benefit businesses, or provide a breakthrough in scientific research.

The application of data mining is very wide. As long as the industry has a database with analytical value and needs, data mining tools can be used for targeted research and analysis. 

Data and content are now at the core of the Internet, be it a traditional industry or a new industry. By spearheading the successful integration with the Internet and the ability to discover hidden laws from the goldmine of big data, you can seize the opportunity to become a symbol of technological reform and reap the benefits. 

Common uses are most common in retail, manufacturing, finance and insurance, communications, and healthcare. There are four main methods of big data mining to add value to a business: first: segmentation of a customer group, and then individual special services for each group. Second: simulate a real-world environment, discover new needs, and increase your ROI. 

Third: to strengthen departmental ties and improve the efficiency of the entire management and production chain. Fourth. Reduce maintenance costs and find the hidden keys to product and service innovation. In theory: all industries will benefit from the development of data mining. Reduce maintenance costs and find the hidden keys to product and service innovation. 

In theory: all industries will benefit from the development of data mining. Reduce maintenance costs and find the hidden keys to product and service innovation. In theory: all industries will benefit from the development of data mining.

For example, the role of data mining in e-commerce is becoming more and more important. It can be used to analyze websites, identify user behavior patterns, retain customers, provide personalized services, optimize website designs, and help e-commerce websites integrate real value knowledge. 

Extracted from a wealth of information to better serve e-commerce website users and guide corporate decisions. Specific E-Commerce Data Mining Applications: E-commerce data mining applications enable you to directly track data, analyze customer buying behavior, and help sellers make business decisions quickly. 

The app in e-commerce marketing is based on the principle of market segmentation in marketing, and its underlying assumption is that past consumer behavior is the best explanation for their future consumption trend. By collecting, processing and processing a large amount of information related to consumer behavior, determine the interests, consumption habits, consumption trends and consumption needs of a specific consumer group or individual, and then infer the following consumption behavior of the corresponding consumer group or individual. 

It is necessary to analyze the product life cycle strategy, market segmentation, formulate reasonable product strategies and pricing strategies,

In the future development trend of data mining, in my opinion, the application of data mining on the web, especially the creation of a data mining server on the Internet, interacts with the database server to implement data mining, thereby creating a powerful data mining engine and Data mining services market. 

Integrate a variety of heterogeneous data mining technologies to enhance the mining of various unstructured data such as text data, graphics data, video image data, audio data, and even integrated multimedia data.

Data mining is a growing field with broad application prospects. With the increasing power of computer processing, the more data you can get, the more value you can get. The continuous repetition of experimentation and the gradual accumulation of big data has allowed humans to discover patterns and predict the future – no longer a way of reading minds in science fiction films. Promote the deeper development and widespread use of data mining technologies to create greater social and economic value.

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