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1. In the K-line chart, each K-line symbol shows the dispute between the long and short sides of the market, indicating the possible change of stock price in the next unit time
A. bareheaded Yang line. Generally speaking, the longer the positive line, the stronger the power of the parties, and the more likely the price will rise in the future
B. glabrous Yin line. Generally speaking, the longer the negative line, the stronger the strength of the air side, and the more likely the price will fall in the future
C. inverted tapered male line. It shows that many parties have advantages. If the upper shadow line is long, it can reflect the pressure on the rise, and it may fall in the future; If the shadow line is short, the market may rise
D. inverted cone negative line. It shows that the air side has an advantage, the price rises first and then falls, and it may fall later
e. the upper and lower shadow lines. It shows that after the long short dispute, many parties have a slight advantage, but the upward momentum is insufficient, and there is a possibility of a correction in the future
F. Yin line with upper and lower shadow lines. It shows that after the long short dispute, the short side has a slight advantage, but the decline meets resistance, and there is a possibility of rebound in the future
G, big cross. Most of them appear before the market reversal, which shows that the long and short sides are equal and the price trend is facing a breakthrough. If the upper shadow line is longer, the possibility of downward breakthrough is greater, and the lower shadow line is longer, the possibility of upward breakthrough is greater
H, small cross. The upper and lower shadow lines are relatively short, which shows that the long and short sides are not willing to enter the market strongly, and most of them are consolidation in the future. 1. Tapered male line. It shows that many parties have the advantage, and if the shadow line is long, the future market rise may be larger
J. tapered Yin line. It shows that the air side has the advantage. If the shadow line is long, it may rebound in the future. If the shadow line is short, it may continue to fall
k, inverted T shape, also known as "tombstone shape". It shows that the stock price is weak and there is a risk of falling in the future
L, T-shaped. It shows that the decline of stock price is blocked, and it is possible to rise in the future
specific can refer to the relevant books to understand in detail, and then use a simulation disk to practice, so that the theory can be quickly and effectively mastered the knowledge, the current niugubao simulation speculation is not bad, many of its functions are enough to analyze the market and indivial stocks, it is much more convenient to use, I hope it can help you, I wish you a happy investment!

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5. 1. Monkey King: rebellious, brave and resourceful, powerful, indomitable, dare to do, hate evil

the monkey king is gifted and intelligent. He has learned all kinds of magic skills. With one breath, he can make 72 changes. He also has a magic weapon, the golden cudgel, which is as small as a needle and as large as a brass stick. Once he drives through the cloud, he can go up and down the world. Wukong is still monkey. He is shrewd and mischievous. The monkey king is not afraid of heaven and earth, so he dares to make trouble with the Dragon King, Hell House and heaven palace, and bet with the Buddha

it can be seen from this that he is rebellious, unwilling to be bound and unconventional. Full of vitality, fearless spirit, down-to-earth character and optimistic personality, he is not afraid of hardship and brave to face challenges. His spirit of adventure and trouble fully shows the characteristics of hero. But being competitive, arrogant and impatient are his weaknesses< Tang Monk: honest and kind, devoted to Buddhism, kind-hearted, timid, pedantic, poor identification ability

a devout and persistent Buddhist is firm in the process of learning Buddhist scriptures, never slacking and wavering, not confused by wealth and color, and not yielding to death. With his indomitable spirit, he finally achieves good results. On the other hand, he is kind-hearted, and even cruel enemies can be forgiven

3. Zhu Bajie: lazy, short-sighted, gossip; Loyal and kind, fighting bravely, correct mistakes, simple and honest

Bajie is lazy, greedy for money and lust, greedy for life and afraid of death. When he meets difficulties, he shouts to break up and can't stick to it. However, he is gentle, honest, simple and sometimes brave; I'm often fascinated by the beauty of monsters. It's hard to tell the enemy from the enemy. He is obedient to his elder martial brother's words, loyal to his master, and has made great contributions to Tang Monk's learning from the Buddhist scriptures. He is a comedian who is loved and sympathized by people< 4. Monk Sha is willing to bear hardships, honest and honest, and has no opinion

monk Sha is relatively honest and loyal. He is not as rebellious as the monkey king, nor as fickle as Bajie. Ever since he gave up his status as a monster, he has been following monk Tang wholeheartedly, upright and selfless, never looking left and right, and abiding by the Buddhist precepts<

extended materials:

journey to the West was written in the middle of Ming Dynasty in the 16th century. Since its publication, it has been widely spread in China and all over the world, and has been translated into many languages. Journey to the west is one of the four classic Chinese novels. It is not only the best myth novel, but also a combination of mass creation and literati creation. The book tells the story of the monkey king who saved the Tang monk from the West and went through ninety-nine and eighty-one difficulties

the novel begins with seven stories of "havoc in heaven", which puts the image of monkey king in the first place in the book. From the eighth to the twelfth chapters, the stories of the Tathagata, Guanyin's visit to the monk, Wei Zheng's beheading of the dragon, and the birth of the Tang Monk are written to explain the origin of the Sutra. From the fourteenth day back to the end of the book, he wrote that Sun Wukong was forced to convert to Buddhism, protected the Tang monks to learn Buddhist scriptures, and with the help of Bajie and Shaseng, he killed demons and Demons all the way to the West

the appearance of journey to the West has opened up a new category of novels about gods and demons. The book will be well intentioned ridicule, spicy satire and serious criticism skillfully combined with the characteristics of a direct impact on the development of satirical novels. It is the peak of ancient romantic novels. In the history of world literature, it is also a masterpiece of romanticism and a pioneer of magic realism

source: Internet journey to the West

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7.

method 1. Visualization analysis

data visualization is the most basic requirement of data analysis tools, whether it is log data analysis experts or ordinary users. Visualization can directly display the data, let the data speak for themselves, and let the audience see the results

method 2. Data mining algorithm

If visualization is used for people to watch, then data mining is for machines. Clustering, segmentation, outlier analysis and other algorithms enable us to dig deep into data and mine value. These algorithms not only deal with a large amount of data, but also rece the speed of processing big data

method 3. Predictive analysis ability

Data Mining enables analysts to better understand the data, while predictive analysis enables analysts to make some predictive judgments based on the results of visual analysis and data mining

method 4. Semantic engine

as the diversity of unstructured data brings new challenges to data analysis, a series of tools are needed to parse, extract and analyze data. We need to design the semantic engine from & lt; Document & quot; It can extract information intelligently

method 5. Data quality and master data management

data quality and data management are some best practices in management. Data processing through standardized processes and tools ensures pre-defined high-quality analysis results

8. Traditional data mining is to find valuable rules in the data, which is consistent with the direction of big data
it's just that big data has the characteristics of "high-dimensional, massive and real-time", that is to say, it has the characteristics of large amount of data, high dimension of data source and data, and rapid update. Traditional data mining technology may be difficult to solve, so we need to improve the algorithm (enhance the processing ability of the algorithm for big data) and the framework of the scheme (decompose tasks, Break down big data analysis into several small units to solve, or extract rules, integrate repeated data, etc.) to improve processing ability
therefore, it can be understood that big data is the scene is the problem, and data mining is the means.

9.

method 1. Analytical visualizations

data visualization is the most basic requirement of data analysis tools, whether they are log data analysis experts or ordinary users. Visualization can directly display the data, let the data speak for themselves, and let the audience see the results

method 2. Data mining algorithms

If visualization is used for people to watch, then data mining is for machines. Clustering, segmentation, outlier analysis and other algorithms enable us to dig deep into data and mine value. These algorithms not only deal with a large amount of data, but also rece the speed of processing big data

method 3. Predictive analytical capabilities

Data Mining enables analysts to better understand the data, while predictive analysis enables analysts to make some predictive judgments based on the results of visual analysis and data mining

method 4. Semantic engine (semantic engine)

as the diversity of unstructured data brings new challenges to data analysis, a series of tools are needed to analyze, extract and analyze data. We need to design the semantic engine from & lt; Document & quot; It can extract information intelligently

method 5. Data quality and master data management

data quality and data management are some best practices in management. Data processing through standardized processes and tools ensures pre-defined high-quality analysis results

sinomeni Xiaobian will share with you what big data mining methods are. If you are interested in big data engineering, I hope this article can help you. If you want to know more about the skills and materials of data analysts and big data engineers, you can click other articles on this website to learn

10. On the afternoon of March 13, Li Tao, Dean and professor of School of computer and software of Nanjing University of Posts and telecommunications, made a theme sharing entitled "data mining in the age of big data" in the micro lecture column of CIO era app, which deeply explained big data and data mining in the age of big data

as we all know, big data mining in the era of big data has become a hot spot in all walks of life
first, data mining
in the era of big data, the generation and collection of data is the foundation, and data mining is the key. Data mining can be said to be the most critical and basic work of big data. Generally speaking, data mining is also known as data mining, or knowledge discovery from data, which generally refers to an engineering and systematic process of mining hidden, previously unknown but potentially useful information and patterns from a large number of data
different scholars have different understanding of data mining, but in my opinion, the characteristics of data mining mainly include the following four aspects:
1. A combination of theory and application: data mining is a perfect combination of theoretical algorithm and application practice. Data mining comes from the needs of the application in the actual proction and life. The data mining comes from the specific application. At the same time, the knowledge found through data mining should be applied to practice to assist the actual decision-making. Therefore, data mining comes from the application practice, but also serves the application practice, data is fundamental, data mining should be data-oriented, which involves the design and development of algorithms, need to consider the needs of practical application, abstract and generalize the problem, apply good algorithms to practice, and be tested in practice
2. An engineering process: data mining is an engineering process composed of multiple steps. The application characteristics of data mining determine that data mining is not only algorithm analysis and application, but also a complete process including data preparation and management, data preprocessing and conversion, mining algorithm development and application, result display and verification, and knowledge accumulation and use. And in practical application, the typical data mining process is an interactive and circular process< A collection of functions: data mining is a collection of multiple functions. Common data mining functions include data exploration and analysis, association rule mining, time series pattern mining, classification and prediction, clustering analysis, anomaly detection, data visualization and link analysis. A specific application case often involves many different functions. Different functions usually have different theoretical and technical basis, and each function has different algorithm support
4. An interdisciplinary field: data mining is an interdisciplinary subject, which makes use of the research achievements and academic ideas from statistical analysis, pattern recognition, machine learning, artificial intelligence, information retrieval, database and many other fields. At the same time, some other fields such as stochastic algorithm, information theory, visualization, distributed computing and optimization also play an important role in the development of data mining. The difference between data mining and these related fields can be summarized by the three characteristics of data mining mentioned above. The most important thing is that it focuses more on application
to sum up, applicability is an important characteristic of data mining, which is the key to distinguish it from other disciplines. At the same time, its application characteristics complement each other with other characteristics. These characteristics determine the research and development of data mining to a certain extent. At the same time, they also provide guidance for how to learn and master data mining. For example, from the perspective of research and development, the demand of practical application is the root of many methods in the field of data mining. From the beginning of customer transaction data analysis, multimedia data mining, privacy preserving data mining, text mining, web mining and social media mining are all driven by applications. Engineering and aggregation determine the research content and direction of data mining. Among them, engineering makes the different steps in the whole research process belong to the research category of data mining. The aggregation makes data mining have a variety of different functions, and how to connect and combine multiple functions, to a certain extent, affects the development of data mining research methods. For example, in the mid-1990s, the research of data mining mainly focused on the mining of association rules and time series patterns. By the end of 1990s, researchers began to study classification algorithms based on association rules and time series patterns (such as classification based on Association), which organically combined two different data mining functions. At the beginning of the 21st century, a hot research topic is semi supervised learning and semi supervised clustering, which is also an organic combination of classification and clustering. In recent years, some other research directions, such as subspace clustering (the combination of feature extraction and clustering) and graph classification (the combination of graph mining and classification), also combine multiple functions. Finally, the crossover leads to the diversification of research ideas and methods
what I mentioned above is the impact of the characteristics of data mining on research development and research methods. In addition, these characteristics of data mining provide guidance on how to learn and master data mining. There are some guidance for training graate students and undergraates, such as application. When guiding data mining, you should be familiar with the business and needs of application, Demand is the purpose of data mining. The close combination of business, algorithm and technology is very important. Only by understanding the business and grasping the demand can we analyze the data and excavate its value. Therefore, in practical application, we need a kind of talents who not only understand business, but also understand data mining algorithm. Engineering determines that to master the data mining needs to have a certain engineering ability, a good data mining personnel is an engineer, has a very strong ability to deal with large-scale data and develop prototype system, which is equivalent to the training of data mining engineers, the data processing ability and programming ability is very important. The aggregation makes it necessary to do a good job in the accumulation of various underlying functions and algorithms in the specific application of data mining. The crossover determines that when learning data mining, we should actively understand and learn the ideas and technologies in related fields
therefore, these features are the characteristics of data mining, through which we can summarize and learn data mining< Second, the characteristics of big data
bigdata is often used to describe and refer to the massive information generated in the era of information explosion. The significance of big data research is to discover and understand the information content and the relationship between information. To study big data, we must first clarify and understand the characteristics and basic concepts of big data, and then understand and understand big data
to study big data, we must first understand the characteristics and basic concepts of big data. It is generally believed in the instry that big data has the standard "4V" characteristics:
1. Volume: a huge amount of data, jumping from TB level to Pb level
2. Variety: there are many types of data, such as web logs, videos, pictures, geographic information, etc
3. Velocity: fast processing speed and real-time analysis, which is essentially different from the traditional data mining technology
4. Value: the value density is low and contains high effective value. Reasonable use of low-density value data and correct and accurate analysis will bring great commercial and social value
the above "4V" characteristics describe the main differences between big data and "small data" of previous partial sampling. However, practice is the only way to embody the ultimate value of big data. From the perspective of the complexity of practical application and big data processing, big data also has the following new "4V" characteristics:
5. Variability: the structure and meaning of data may change in different scenarios and research objectives. Therefore, specific context should be considered in practical research
6. Veracity: obtaining real and reliable data is the premise to ensure the accuracy and effectiveness of the analysis results. Only the real and accurate data can get the real meaningful results
7. Volatility / variance: because the data itself contains noise and the analysis process is not standardized, different algorithms or different analysis processes and means will lead to unstable analysis results
8. Visualization: in the big data environment, data visualization can more intuitively explain the meaning of data, help to understand data and interpret results
to sum up, the above "8V" features have strong guiding significance in big data analysis and data mining< In the era of big data, data mining needs to consider the following four issues:
the core and essence of big data mining is the organic combination of application, algorithm, data and platform
because data mining is application driven and comes from practice, massive data is generated in applications. Driven by specific application data, supported by algorithms, tools and platforms, the knowledge and information found will be applied to practice, so as to provide quantitative, reasonable, feasible and valuable information
it is necessary to design and develop corresponding data mining and learning algorithms to mine the useful information hidden in big data. The design and development of the algorithm need to be driven by specific application data, and be applied and verified in practical problems. The implementation and application of the algorithm need an efficient processing platform, which can solve the problem of volatility. Efficient processing platform needs effective analysis of massive data, timely integration of multiple data, powerful support for the implementation of data-based algorithms and data visualization, and standardize the process of data analysis
in a word, the idea of combining application, algorithm, data and platform is a comprehensive refinement of the understanding and understanding of data mining in the era of big data, which embodies the essence and core of data mining in the era of big data. These four aspects are also the integration and architecture of the corresponding research aspects. These four architectures are carried out from the following four levels:
application layer: it is concerned with data collection and algorithm verification, and the key problem is to understand the semantic and domain knowledge related to the application
data layer: data management, storage, access and security are concerned with how to use data efficiently
algorithm layer: mainly the design and implementation of data mining, machine learning, approximation algorithm and other algorithms
platform layer (Infrastructure): data access and computing, computing platform processing distributed large-scale data
to sum up, data mining algorithms are divided into multiple levels, and there are different research contents at different levels. We can see the main research directions in data mining, such as using data fusion technology to preprocess sparse and heterogeneous data