Fuzzy sets for data mining and recommendation algorithms

Abstract

A fuzzy system is a system that defines input, output, and state variables on a fuzzy set and is a generalization of a deterministic system. The fuzzy system begins at the macro level and covers the fuzzy features of human brain thinking. It has advantages in describing advanced knowledge. Fuzzy sets can mimic the comprehensive conclusions of people solving fuzzy information problems, which are difficult to solve by conventional mathematical methods, so computer applications can be extended to humanities, social sciences and complex systems. In this way, it can better solve nonlinear problems and is widely used in automatic control, decision analysis, time series signal processing, economic information systems, medical diagnostic systems, and earthquake prediction systems. This paper aims to study the data mining algorithm of fuzzy systems based on fuzzy sets. By using the powerful fuzzy data modeling function of fuzzy theory, it combines with other intelligent processing methods, and makes practical use in industrial life. By comparing the application of fuzzy set data mining and algorithm, it is found that in the application state, the economic benefits of the factory are improved by 36% and the production efficiency is improved by 25% under the application of data mining and algorithm. The research data shows that the data mining and recommendation algorithms of fuzzy sets are beneficial to the development and operation of the factory. The research results show that compared with the conventional production and processing plan, the technology uses fuzzy set theory to transform the fuzzy attributes, which is more advantageous in scientific and technical systems and algorithms with its scientificity, accuracy, innovation and extensiveness.

Keywords

Data mining recommendation algorithm fuzzy system fuzzy set

1 Introduction

With the continuous development of research in the field of mathematical theory in China, and the research on the use of several special modules such as collection concepts and theories, people are paying more and more attention to the related problems in the field of fuzzy sets. In the objective world, there are many vague phenomena that are not the same. A regular collection is an entire object with a specific attribute, and the concepts expressed by this attribute must be clear and well defined. Therefore, the attribution of each object to the set is also obvious, one or two [1 –3]. But in people’s minds, there are many vague concepts, such as lenient, small, cold, early morning. The object properties described by these concepts cannot simply be answered with “yes” or “no”, all attribute objects are blurred. Concept descriptions, also known as fuzzy sets or fuzzy subsets [4 –6]. Since the concept itself is unclear and not clearly defined, the affiliation of the subject in the collection is also unclear. This definition was first proposed by cybernetic expert LA Zadeh in 1965 and graduated from the University of California. The method is represented by the research object and the fuzzy concept, which is reflected as a fuzzy set, and the corresponding membership function is established by the following method: the means of fuzzy set, the operation is transformed, and the fuzzy object is analyzed [7]. Fuzzy set theory is based on fuzzy mathematics to study inaccuracy. The emergence of this concept makes mathematical ideas and methods can also be used to deal with fuzzy phenomena [8, 9], thus forming fuzzy set theory (commonly called fuzzy mathematics basis). Since the concept of fuzzy set theory was born, the theory of fuzzy sets and fuzzy systems has been systematically and rapidly developed. After that, many mathematicians and scientists began to extend fuzzy sets to different degrees and different contents. In this process, the most common theoretical extension of modular sets is a set of working tools [10], in which the study of special sets of modules (such as intuitionistic fuzzy sets and interval module sets) is the most in-depth. In the mid-1970 s, Zade proposed the principle of extension from the perspective of category theory, and proved the rationality of this principle, which laid a solid foundation for the development of fuzzy set theory. The concept of collection in the mathematical theory system is a very important history, and the special fuzzy set is a new mathematical concept developed in the traditional concept of collection [11]. In this paper, we study fuzzy systems - research data mining algorithms and suggestions based on fuzzy sets, and analyze the use of fuzzy sets [12].

The development of fuzzy set theory is a long-term process. In this process, mathematicians and scientists begin to define intuitionistic fuzzy sets as three-digit fuzzy sets. On this basis, intuitionistic fuzzy sets and special types of fuzzy sets are formally established. And establish the relationship between fuzzy sets. In addition, in the development of arbitration theory, mathematicians and scientists provide the basis and theorem for an effective intuitive model; therefore, in the process of assigning the related properties of the intuitionistic fuzzy set principle, it is further extended to the complex function intuitionistic fuzzy set. Principle. In fact, collection is a high degree of abstraction of certain objective things, because the inherent characteristics of human knowledge have its incompleteness, uncertainty and inconsistency. Therefore, this ambiguity can be seen everywhere in the data source: on the one hand, in a conventional data source, it is difficult to accurately determine the correlation between different value points or value ranges of each attribute because these are not necessary, so people Often concerned with certain associations with a higher degree of abstraction; on the other hand, due to the diversity and complexity of the objective world [13, 14], people’s understanding of many things is based on fuzzy concepts, some properties of events Fuzzy concepts must be used to abstract and generalize. The fuzzy set theory proposed by Zadeh provides a mathematical basis for the description and processing of such fuzzy information. Fuzzy dialing is an extension of traditional dialing. When the value of the membership function in the fuzzy concept is 0, 1, the fuzzy set degenerates into a traditional set, so when extracting the data source, we can introduce the fuzzy concept for the specified attribute. And use fuzzy sets to convert attribute values so that the attribute values in the data source are beneficial for human understanding and computer analysis. This paper introduces the fuzzy concept for each attribute, establishes the corresponding membership function, and then performs additional scanning on the data source, and replaces the value of each attribute with the corresponding participation degree of each event.

The innovation of this paper is to combine it with other intelligent processing methods by making full use of the powerful functions of modeling fuzzy data in fuzzy theory. The data mining algorithm based on fuzzy system is studied and the related suggestions of fuzzy set are proposed. In this paper, the concept of ambiguity is introduced first, the data source is scanned, and the value of each attribute in each event is replaced with the corresponding membership degree. Therefore, the converted data source has: (1) The specific attribute a_k represents the degree of membership of the fuzzy concept in any event, and (2) The average value of the attribute in the data source reflects the overall level of the data source relative to the fuzzy concept, for example: The average of the Light Recovery = High field indicates the overall output level of the data source. Data mining is a process of extracting useful knowledge from a large amount of data. From simple structure to complex structure, from order to disorder, from accuracy to ambiguity, from concentration to distribution, data problems can be effectively solved. And use data mining to integrate databases and apply them to machine learning, artificial intelligence, statistics and other discipline technologies to extract useful data.

The first part of this paper introduces the research and recommendation algorithm of fuzzy data mining based on fuzzy data, as well as the innovation of this paper and the organizational structure of this paper. The first section of the second section introduces the relevant research in this field, the second section introduces the methods of data analysis and the algorithms that recommend fuzzy sets, and the third section introduces the algorithms that actively use data mining and recommend fuzzy sets. The third part has experimental content, the first section introduces the specific settings and data collection of the experiment, the second section introduces the entire test procedure, and the third section introduces the basic test procedure. The fourth part first analyzes the application results and advantages of the fuzzy set recommendation algorithm, and then analyzes the fuzzy set, and obtains the performance advantages and suggestions of the data mining algorithm and the recognition of people. The fifth part summarizes the full text.

2 Proposed method

2.1 Related work

Since many factors influence the stock price, it has a non-linear structure, so trading in forecasting and recommending exchanges is a very difficult task [15, 16]. In a user-friendly way, Wang proposed a new forecasting system and stock trading advice. The recommendation system can inform the user about the stock trading information in the next step, and use the detailed information to convert the initial time series into meaningful and the explained particles also provide a more efficient way to predict heterogeneous parts. The system first determines the interval based on the detailed information, determines the fuzzy set and erodes the historical data, then establishes the fuzzy relationship and assigns weights for each time period, and finally performs prediction and recommendation [17]. The experimental results show that the system has the best predictive index and increases the possibility of profit. Zhao Xiaoqiang applied vector fuzzy support machine (FSVM) data mining to training sets with large numbers of samples and classification slow classification defects. Vector algorithm is used in the improved data mining fuzzy classifier. It is the first efficient algorithm to support candidate vectors, which reduces the size of the training set to improve the learning speed. Secondly, a new fuzzy membership function is proposed. The hyperplane effect of the auxiliary vector on the optimal classification of fuzzy auxiliary vector is used, and the pre-selected training set is used for learning [18, 19]. Finally, the particle swarm optimization algorithm is used to select the best subset of the support vector, and the average classification error is obtained. Used as a fitness function. When the last particle is output, the membership in the sample is compared to the set threshold, and the support vector with a relatively high membership is selected as the new reference vector to improve the classification speed. The experimental results show that the proposed algorithm improves the learning speed and classification speed of fuzzy support vectors without loss of classification accuracy.

2.2 Fuzzy set

(1) Principle of fuzzy set

In fact, collections are highly abstractions of certain objective things, and fuzzy sets S must be a collection of such objects: for any object, in addition to two possible possibilities, it still exists. “Can determine exactly whether object O belongs to set S” or “to some extent, object O belongs to set S”, this “belonging” state is between “fully owned” and “completely not owned”, this It is the ambiguity of the set S. Since the inherent characteristics of human knowledge are its incompleteness, uncertainty and inconsistency, this ambiguity can be seen anywhere in the data source. On the one hand, in general data sources, the number of values of the quantity attribute is very large, it is difficult to accurately find the relationship between different value points or value ranges of each attribute, which is not necessary, so people usually pay attention to the other side. Due to the diversity of the objective world and complexity, people’s understanding of many things is based on fuzzy concepts, so we must use fuzzy concepts to abstract and generalize some attributes of events. Zade’s set theory can introduce the concept of ambiguity for specified attributes when retrieving data sources. Used to convert attribute values, so the attribute values in the data source are easy for human understanding and computer analysis and treatment. For ease of description, this article is for each attribute. It should be noted that when determining the membership function, it should be determined according to the actual situation of the problem. The fuzzy subset A in this universe means that for any x∈A, a quantity μA(x) is defined, where μA(x) is x Members in A, while μA(x)∈[0,1]; mapping X⟶[0,1], x⟶μA(x) is called membership function A, and fuzzy subsets are often called fuzzy sets or Fuzzy set. In the moving target detection application, each attribute is a fuzzy set, and any fuzzy set corresponds to a membership function. Therefore, in order to truly describe various attribute values, it is very important to select an appropriate membership function model.

2.3 Research on data mining and recommendation algorithm

(1) Introduction of data mining

When studying knowledge engineering, there are always problems such as information ambiguity. There are three kinds of ambiguity: term ambiguity (such as high and low), data uncertainty (such as noise), and knowledge uncertainty (such as rules before and after rules) between the dependencies). One of the basic theories of artificial intelligence is not enough to solve these uncertainties. In this paper, fuzzy set theory is used to transform fuzzy attributes, and fuzzy association rules in data sources are obtained. A fuzzy association rule retrieval algorithm is proposed and applied to refining. One aspect of the industry can further explore the role of fuzzy set theory in data mining to improve the quality of data mining. In recent years, the application of fuzzy set theory in data mining research has become a hotspot in data mining research. In order to track the progress of research and study the future research direction, we consider the main research directions of fuzzy set theory in data mining (cluster analysis, association analysis, classification), mainly to explain the representation of data and patterns, and the calculation mode. Key issues such as similarity can be seen in the use of powerful fuzzy data modeling functions in fuzzy data theory, combined with other intelligent processing techniques is the main technology in this field. Data mining solves the problem of further developing knowledge-based intelligent systems because of the lack of knowledge and the difficulty of acquiring knowledge.

(2) The development of data mining

As the total amount of information continues to grow, there is an urgent need for an effective information analysis tool that can detect hidden relationships between large amounts of data and extract useful information knowledge from large amounts of data. Despite the emergence of simple data statistics methods, modern intelligent data analysis tools are not yet mature. Therefore, there is a big gap between data generation and understanding. Data mining is a new type of data mining technology that can solve this controversy. The purpose of data mining is to extract hidden prediction information from large data sources (also known as discovery knowledge), which can explore potential patterns between data and identify information that business operators may miss in the form of understanding and observation. An important aspect of data mining applications is the identification of conventional, new, useful, and understandable patterns from large amounts of raw data. Over the past few decades, fuzzy set theory has been successfully used for pattern recognition, intelligent control, and machines. Learning, artificial intelligence and other areas of research. For a long time, the expression of knowledge and the rationality of knowledge have always been the main direction of research in the field of fuzzy set theory. The result provides a theoretical basis for the construction of knowledge-based intelligent design systems. In the face of a large amount of data, it cannot be collected manually. When sorting and using traditional data analysis processing tools to acquire knowledge to solve this problem, the emergence of data mining has opened up a new way for knowledge acquisition. It adapts to other technologies such as databases, intelligent learning, and artificial development technologies, and draws useful knowledge from it, greatly severing the problem of knowledge-based intelligent systems.

(3) Recommendation algorithm

With the creation and development of fuzzy sets, fuzzy reasoning has become an important part of the field of computational intelligence. Regarding the main problems of fuzzy thinking, scientists at home and abroad have conducted extensive research and proposed various fuzzy inference algorithms, which are all intelligent algorithms. Reasoning is one of the main characteristics of human intelligence, and an important technology to realize artificial intelligence. With the creation and development of fuzzy sets, fuzzy reasoning has become an important part of the field of computational intelligence. Fuzzy thinking aims to model human thinking models and build incorrect thinking methods. The reasoning process is based on rules and given facts, so people can ask questions and a rough answer based on incorrect and imperfect knowledge base. Currently, fuzzy inference technology is widely used in many fields of intelligent systems, such as fuzzy control systems, fuzzy expert systems, fuzzy neural network systems and fuzzy decision support systems.

2.4 Research on data mining and recommendation algorithm of fuzzy sets

(1) In the excavation process of any scheme I, if the support speed is greater than or equal to the minimum support speed, the support speed of the non-empty sub-mode must also be greater than or equal to the minimum support speed, which is to check the candidate template, reduce the number of candidate templates, and speed up the excavation speed. And plays an important role in reducing storage capacity.

(2) Algorithm Description: First, initialize the data structure of the frequent mode, use the Match function to find all frequent patterns of length k, use the Candidates function to generate the appropriate frequent patterns, and associate all fuzzy attributes of the pattern with the rules. Moreover, the algorithm can be improved and simplified, that is, when the frequent template is generated for the first time, it is only used for searching and generating in the first attribute, and when the candidate frequent pattern is generated for the kth time, only the kth frequent candidate mode and the first k, combining+1 attributes to create possible candidate templates, and not using any other attributes after the k + 1 attribute, which can significantly speed up the mining and reduce the number of possible templates.

(3) Algorithm example

Definition one: Fuzzy mode $P = A_{1} \cap A_{2} \cap \cdot \cdot \cdot \cap A_{k}$ (1)

The length of the mode P is called k, and AK is called the item: $A_{i} = (a_{i} : t_{i}) a_{i} \in A, t_{i} \in v_{i} (i = 1, 2, \cdot \cdot \cdot, m)$ (2)

The mode consisting of any q(q < k) items from $A_{1}$ , $A_{2}, . . ., A_{k}$ is called the q sub-mode of P. Definition two: Extend the project Ai, $A_{i} = (a_{i} ⩾ t_{i})$ (3)

Indicates that the membership of the fuzzy attribute ai is greater than or equal to ti. This definition plays an important role in mining higher-level association rules.

Definition three:

1) Take the fuzzy sample of the data source, calculate the mutual information of each feature according to the fuzzy value, and get the exact value.

2) Select the maximum mutual information feature Ak;

3) The example of taking the same value at Ak is divided into the same sample subset, and the category result of the sample subset is fuzzy.

4) A recursive call-building algorithm for a subset of multiple fuzzy samples.

5) Limit the fuzzy category.

If the subset contains only the example of the fuzzy category, the corresponding decision tree is marked with C1, C2, C3, and returns to the call. The calculation process of the fuzzy algorithm is as follows:

1) Calculating information entropy entropy H (U) $H (U) = - \sum_{i} P (u_{i}) {log}_{2} p (u_{i})$ (4)

Among them, $P (u_{i}) = | u_{i} | / | S |$ . Indicates the total number of training sample subsets S, |u_i|indicates the number of instances where the sample ambiguity value is a category.

2) Conditional entropy H (U ∖ V)

$\begin{matrix} H (U ∖ V) \\ = - \sum_{i} P (v_{i}) \sum_{i} P (u_{i} | v_{i}) {log}_{2} P (u_{i} | v_{i}) \end{matrix}$ (5)

When the attribute Ai takes the value v_i, the conditional probability of the category u_i is: $P (u_{i} | v_{i}) = | u_{i} | / | v_{i} |$ (6)

3) Calculate mutual information. $I (A_{1}) = H (U) - H (U | V)$ (7)

4) Choose larger I(A_i), Recursively establish a decision tree.

Definition IV: In order to solve the shortcomings of the CRI method, Wang Guojun proposed the full implication of the fuzzy inference from the perspective of logical semantic implication. The core of this algorithm is the three I principle of fuzzy reasoning.

The solution B* of the FMP problem is the smallest fuzzy set in the domain Y that makes the following formula take the maximum value: $(A (x) \to B (y)) \to (A^{*} (x) \to B^{*} (y))$ (8)

The solution to the FMT problem $\overset{*}{A}$ is the largest fuzzy set in the domain X that makes the above formula take the maximum value.

The solution B^* of the FMP problem is the largest fuzzy set in the domain Y that makes the following formula take the maximum value. $(A^{*} (x) \to B^{*} (y)) \to (A (x) \to B (y))$ (9)

3) y The ∂-B* of the FMP problem is the largest fuzzy set in the domain y that makes the following formula hold: $(A^{*} (x) \to B^{*} (y)) \to (A (x) \to B (y)) ⩾ \partial$ (10)

4) Turksen give a formula for calculating similarity: $S_{AARS} (A, A^{*}) = (1 + DM (A, A^{*}))^{- 1}$ (11)

3 Experiments

3.1 Experimental setup

(1) Data collection

The concept of fuzziness comes from the fuzzy linearity unique to the display world. It makes full use of the powerful fuzzy data modeling function of fuzzy data theory and combines it with other intelligent processing methods. Many algorithms for intelligent analysis of fuzzy association rules are proposed. Fuzzy computing is based on fuzzy set theory, which can simulate the inexact and non-linear ability of human brain processing information and is used in many fields. On the other hand, fuzzy dictionary is used to describe the numerical values and general properties of relational databases, and then a kind of the algorithm is used to develop fuzzy association rules for mixed data, which effectively eliminates the obstacles caused by knowledge bottlenecks and obtains the knowledge connotation. The intelligent system further promotes the development of technology, makes the quality more efficient, and makes the decision tree of mining results more scientific.

(2) Experimental steps

Determination of fuzzy sets.

Data mining of fuzzy sets.

Recommendation algorithm for fuzzy sets

Data mining application testing.

Start the experiment.

Collect experimental data.

3.2 Experimental process

(1) Using data collection algorithms and fuzzy set recommendations as the main techniques, using dedicated input and output devices to measure the economics of the plant, and then comparing the data. For example, an oil refinery prepares an appropriate production plan based on the physical properties of the crude oil and the conditions of the refinery, and prepares and adjusts the production and refining plan based on the actual conditions of the existing crude oil, and then estimates which type of crude oil to use, and which refining plan. It can provide high product productivity and high economic efficiency, calculate production efficiency before and after fuzzy data mining algorithms and recommendations, and the yield will change slightly. Through the comparison and improvement of experiments, the data comparison model is used before and after. The results of fuzzy data mining are more vivid and vivid, which provides powerful technical support for data mining efficiency, improves the quality of data mining, and makes the results more professional.

4 Discussion

4.1 Comparison of data analysis

(1) Fis refers to a fuzzy inference system, also known as a fuzzy system, which has the ability to process fuzzy information based on methods such as fuzzy set theory and fuzzy inference. The structure of the fuzzy set depends on two aspects: the corresponding domain and the corresponding membership function. The description of the membership function is subjective, which means that different people describe the same concept, and the resulting membership function may be very different. As shown in Table 2 of Fig. 1.

Table 1
Data source with fuzzy set conversion

Data source D fuzzy set conversion A B C

0 0 0

0 0 0.9

0 1 0.94

1 1 0.92

1 1 0.97

Table 2

Data comparison analysis table

	Economic benefit	Production processing speed	Product output
Data mining and recommendation algorithm using fuzzy sets	0.87	0.37	0.80
Data mining and recommendation algorithm without fuzzy sets	0.51	0.62	0.53

Fig.1

Production speed and output table.

(2) Data mining is the process of finding target data from massive data. Massive data has a large number of interference options, with fuzzy features and randomness, the location of the target data is unknown, hidden in massive data and the category is unknown, so it is difficult to completely find the target data using traditional data filtering methods. Data analysis and fuzzy set based algorithms facilitate them. Among them, D1 and D2 are data mining, and the fuzzy set is gradually used in the middle. Before using D3, you can see that the improved algorithm in KDD database is very short, can handle small CUP2011 sub-database, and the detection accuracy is higher, reaching 95% feasible. Sex, showing an improved test result algorithm, as shown in Table 3 of Fig. 2.

Fig.2

Fuzzy set algorithm test result.

4.2 Data mining and recommendation algorithm based on fuzzy sets

(1) Data mining refers to extracting unknown, valuable models or laws from large amounts of data as the total amount of information increases. Despite the simple method of data statistics, modern intelligent data analysis tools are not yet mature. Due to the urgent need for effective information analysis tools, a large number of hidden relationships between data are found, and useful information or knowledge is extracted from a large amount of data. Then there is the fuzzy set mining. Extract legal, new, useful, and easy-to-understand templates from large amounts of raw data. At the same time, it is also widely used in the refining industry. After gradually introducing data mining algorithms and fuzzy set recommendations, the factory sales changes are shown in Fig. 3.

Fig.3

Sales record.

(2) In fact, large data sets have complex data relationships, highly nonlinear and ubiquitous noise data, which makes the application of some algorithms very limited and cannot effectively express the relationship between data. This has certain limitations. In order to study and implement the fuzzy data mining algorithm, the fuzzy dial is used to represent the relationship between data, and the data source is transformed to expand the range of representation rules. The system has higher prediction performance, improved classification speed, and reduced Storage capacity, etc. In the case of random surveys of 100 people of different ages, the fuzzy data analysis algorithms and suggestions are satisfactory. The survey results are shown in Fig. 4.

Fig.4

Fuzzy set data mining and recommendation algorithm satisfaction survey.

5 Conclusions

In order to solve the nonlinear problem better and make practical research on industrial life and so on, various fuzzy inference algorithms are proposed. The fuzzy algorithm is designed to model the human thinking model and establish the correct reasoning method. The reasoning process is based on rules and given facts. The powerful fuzzy data modeling function of fuzzy theory has been used to improve and optimize, which has brought about a great improvement in industrial production efficiency, efficiency and daily life application.

Data mining based on fuzzy sets can detect hidden relationships between large amounts of data, extract useful information or knowledge from a large amount of data, transform fuzzy attributes into very abstract moments of certain objective things, track the progress of research and conduct research. By comparing the application of fuzzy set data mining and algorithm, it is found that the factory economic benefits are higher and the production efficiency is greatly improved under the application of data mining and algorithm.

The development of fuzzy set theory is a long-term process. This paper discusses fuzzy systems - data extraction algorithms and suggestions based on fuzzy sets. These algorithms transform data from simple structures to complex structures, from ordered to unordered, from accuracy to accuracy. Ambiguity, from concentration to distribution, due to the lack of bottlenecks in knowledge and knowledge acquisition, it can effectively solve the problem of further development of knowledge-based intelligent systems, and use data mining to integrate databases. The use of Internet learning, artificial intelligence innovation, statistics and other disciplines to extract data to obtain useful knowledge and practical ability in more fields has brought many conveniences for people’s life and industrial development.

Footnotes

Acknowledgments

This work was supported by Natural Science Foundation of Heilongjiang Province (Grant No. LH2019F046), Harbin science and technology innovation talents research project (Grant No. 2016RAQXJ013) and research project of Heilongjiang Education Department (Grant No. 12513060).

References

Ali

and Smarandache

, Complex neutrosophic net, Neural Computing & Applications 28(7) (2017), 1–18.

Ren

, He

, Girshick

, Zhang

and Sun

, Object detection networks on convolutional feature maps, IEEE Transactions on Pattern Analysis & Machine Intelligence 39(7) (2017), 1476–1481.

Mazandarani

, Pariz

and Kamyad

A.V.

, Granular differentiability of fuzzy-number-valued functions, IEEE Transactions on Fuzzy Systems 26(1) (2018), 310–323.

Critchfield

T.S.

and Reed

D.D.

, The fuzzy concept of applied behavior analysis research, Behavior Analyst 40(5) (2017), 1–37.

Cheng

and Bernstein

, Fuzzy expected utility, Fuzzy Sets & Systems 20(2) (2017), 163–173.

Liu

X.L.

, Jia

S.W.

and Gu

, Empirical analysis of the user reputation and clustering property for user-object bipartite networks, International Journal of Modern Physics C 30(5) (2019), 647.

Miskei

, Gregus

, Sharma

, Duro

, Zsolyomi

and Fuxreiter

, Fuzziness enables context-dependence of protein interactions, Febs Letters 591(17) (2017), 2682–2695.

Wang

J.W.

, Li

H.X.

and Wu

H.N.

, A membership-function-dependent approach to design fuzzy pointwise state feedback controller for nonlinear parabolic distributed parameter systems with spatially discrete actuators, IEEE Transactions on Systems Man & Cybernetics Systems 47(7) (2017), 1486–1499.

Zhang

, Wang

, Zhang

and Wang

, Multi-fault fuzzy diagnosis for complicated system based on grey theory, Journal of Beijing University of Aeronautics & Astronautics 43(9) (2017), 1832–1840.

10.

Yeom

, Miller

M.A.

and Delp

, Constructing a teaching philosophy: Aligning beliefs, theories, and practice, Teaching & Learning in Nursing 13(3) (2018), 131–134.

11.

Liu

, An improved clustering-based collaborative filtering recommendation algorithm, Cluster computing, 20(2) (2017), 1281–1288.

12.

D.F.

and Wan

S.P.

, Minimum weighted minkowski distance power models for intuitionistic fuzzy madm with incomplete weight information, International Journal of Information Technology & Decision Making 16(2) (2017), 1–22.

13.

Wang

, Zhang

J.G.

, Peng

W.S.

and Yang

L.C.

, Hierarchical response surface modeling and reliability analysis of complex mechanical system based on composite function, Acta Armamentarii 39(3) (2018), 590–597.

14.

Kim

E.J.

and Hollerbach

, Signature of nonlinear damping in geometric structure of a nonequilibrium process, Physical Review E 95(2-1) (2017), 022137.

15.

Lewis

N.A.

and Earl

, Seeing more and eating less: Effects of portion size granularity on the perception and regulation of food consumption, Journal of Personality & Social Psychology 114(5) (2018), 786–803.

16.

Wang

and Mishra

K.K.

, A novel stock trading prediction and recommendation system, Multimedia Tools & Applications 77(3) (2017), 1–13.

17.

Buczak

A.L.

and Guven

, A survey of data mining and machine learning methods for cyber intrusion detection, IEEE Communications Surveys & Tutorials 18(2) (2017), 1153–1176.

18.

Lee

, Lee

and Song

, Speed-interactive treadmill training using smartphone-based motion tracking technology improves gait in stroke patients, Journal of Motor Behavior 49(6) (2017), 1–11.

19.

Yoon

D.H.

, Kang

, Kim

H.J.

, Kim

J.S.

, Song

H.S.

and Song

, Effect of elastic band-based high-speed power training on cognitive function, physical performance and muscle strength in older women with mild cognitive impairment, Geriatrics & Gerontology International 17(5) (2017), 765–772.

Data source D fuzzy set conversion	A	B	C
	0	0	0
	0	0	0.9
	0	1	0.94
	1	1	0.92
	1	1	0.97

Fuzzy sets for data mining and recommendation algorithms

Abstract

Keywords

1 Introduction

2 Proposed method

2.1 Related work

2.2 Fuzzy set

2.3 Research on data mining and recommendation algorithm

2.4 Research on data mining and recommendation algorithm of fuzzy sets

3.1 Experimental setup

3.2 Experimental process

4 Discussion

4.1 Comparison of data analysis

Table 1 Data source with fuzzy set conversion Data source D fuzzy set conversion A B C 0 0 0 0 0 0.9 0 1 0.94 1 1 0.92 1 1 0.97

Footnotes

Acknowledgments

References

Table 1
Data source with fuzzy set conversion

Data source D fuzzy set conversion A B C

0 0 0

0 0 0.9

0 1 0.94

1 1 0.92

1 1 0.97