Rule Data Mining Research Articles

A Review of the Text and Data Mining (TDM) Rules on Trainable Datasets for AI in China

A Review of the Text and Data Mining (TDM) Rules on Trainable Datasets for AI in China

E-Commerce Data Mining Analysis based on User Preferences and Assiciation Rules

Improving the sales of e-commerce platforms is the primary goal of this paper. This paper studies the data of e-commerce product recommendations from the perspective of user preference and association rules. The characteristics of positive and reverse association rules in data mining are analyzed. Then, a multi-dimension association rule calculation method is proposed. Create a data attribute unit set. By analyzing each attribute's weighted coefficient and similarity, the attribute confidence degree is obtained, and the data is preprocessed. An example is given to verify the effectiveness of the proposed method. The recommendation engine based on user preferences and association rules significantly improves the accuracy, recall rate and prediction coverage of e-commerce recommendation systems.

A Hybrid Particle Swarm Optimization and C4.5 for Network Intrusion Detection and Prevention System

Network Intrusion Detection and Prevention systems (NIDPS) ensure network security and used to effectively detect various attacks and completely stop them from intruding over a network. Since, securing sensitive information carried by various organizations is much more significant, developing enhanced security models become inevitable. To meet the growing demand in safeguarding the network from various known and unknown attacks. In this paper, a Hybrid Particle Swarm Optimization and C4.5 (HPSOCM) method is proposed to network based intrusion prevention system to detect unknown attacks and a signature based SNORT method to detect the known attacks in NIDS. In the hybrid method, we use data mining approach to mine the unknown attacks. Hence, we develop an anomalous detection model and train it using the data mining rules. The trained network is capable of detecting various unknown attacks. The conventional signature based SNORT method detects the known attacks by matching the detected threats from the KDD99 dataset. The proposed HPSOCM method is demonstrated using simulation and the performances were evaluated in terms of Accuracy, Specificity, Detection Rate and False Alarm Rate. The proposed method had produced better efficiency compared to various other existing methods.

Spatiotemporal deformation characteristics of Outang landslide and identification of triggering factors using data mining

Since the impoundment of Three Gorges Reservoir (TGR) in 2003, numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall. One case is the Outang landslide, a large-scale and active landslide, on the south bank of the Yangtze River. The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics. Data mining technology, including the two-step clustering and Apriori algorithm, is then used to identify the dominant triggers of landslide movement. In the data mining process, the two-step clustering method clusters the candidate triggers and displacement rate into several groups, and the Apriori algorithm generates correlation criteria for the cause-and-effect. The analysis considers multiple locations of the landslide and incorporates two types of time scales: long-term deformation on a monthly basis and short-term deformation on a daily basis. This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors. The data mining results reveal different dominant triggering factors depending on the monitoring frequency: the monthly and bi-monthly cumulative rainfall control the monthly deformation, and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide. It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslide-prone areas.

Cluster Barang Elektronik Mengguanakan Algoritma Fuzzy C-Means dengan Optimize Parameter Grid

Electronic products are goods that are really needed at this time, because electronic goods really help humans in carrying out various daily activities, such as television, computers, cellphones, etc. The problem is how to apply the Fuzzy C-means method with Optimize Parameter Grid in the form of grouping electronic goods data for the needs of consumers used, and how to determine the optimum number of clusters from the use of the method used in grouping electronic goods data sets to find the best accuracy value. The purpose of this study was to apply the use of the fuzzy c-means algorithm in the case of grouping electronic data and produce an output to find out the best value of the electronic data used. While this research uses one method, namely the fuzzy cmeans algorithm with Optimize grid parameters which are included in the grouping rules in data mining. The research results are expected to be able to find the best electronic data set grouping based on the Davies Bouldin Index value resulting from the analysis of the fuzzy c-means algorithm at measure_type : NumericalMeasure, Dbi = 0.516 and measure_type : MixedMeasure, Dbi = 0.627.

Data mining-based recommendation system using social networks-an analytical study.

In the current age, social media is commonly used and shares enormous data. However, a huge amount of data makes it difficult to deal with. It requires a lot of storage and processing time. The content produced by social media needs to be stored efficiently by using data mining methods for providing suitable recommendations. The goal of the study is to perform a systematic literature review (SLR) which finds, analyzes, and evaluates studies that relate to data mining-based recommendation systems using social networks (DRSN) from 2011 to 2021 and open up a path for scientific investigations to enhance the development of recommendation systems in a social network. The SLR follows Kitchenhem's methodology for planning, guiding, and reporting the review. A systematic study selection procedure results in 42 studies that are analyzed in this article. The selected articles are examined on the base of four research questions. The research questions focus on publication venues, and chronological, and geographical distribution in DRSN. It also deals with approaches used to formulate DRSN, along with the dataset, size of the dataset, and evaluation metrics that validate the result of the selected study. Lastly, the limitations of the 42 studies are discussed. As a result, most articles published in 2018 acquired 21% of 42 articles, Whereas, China contributes 40% in this domain by comparing to other countries. Furthermore, 61% of articles are published in IEEE. Moreover, approximately 21% (nine out of 42 studies) use collaborative filtering for providing recommendations. Furthermore, the Twitter data set is common in that 19% of all other data sets are used, and precision and recall both cover 28% of selected articles for providing recommendations in social networks. The limitations show a need for a hybrid model that concatenates different algorithms and methods for providing recommendations. The study concludes that hybrid models may help to provide suitable recommendations on social media using data mining rules.

Abnormal Operation Detection of Access Equipment in Dispatching Exchange Network Based on Data Mining

To meet the requirements of a power dispatching service, a dispatching switching network has been widely used instead of circuit switching technology. However, with the growth of the number of access devices, the problem of network security is becoming more and more serious. At present, the commonly used access equipment abnormal operation detection method mainly relies on a two-stage clustering method to identify the abnormal operation state, but the processing effect is not good in the face of a large number of data, resulting in a low F1 value of the final detection result. Therefore, aiming at the access equipment of the dispatching exchange network, an abnormal operation detection method based on data mining is proposed. Scapy, a network tool, is used to capture the traffic data of access devices, and the collected data are normalized and fused. Data feature information is extracted through time sliding window and incremental calculation mode. With the clustering algorithm and related rules in data mining as the core, the abnormal operation detection method of access equipment is designed to obtain accurate abnormal detection results. Finally, based on the recursive graph structure, the detection results are visualized. The experimental results show that the average F1 value of the detection results of the proposed method is 0. 98, which indicates that the detection results of the proposed method have higher accuracy, and based on this, the network security isolation is carried out, which effectively improves the security of dispatching switching network.

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

It is crucial, while using healthcare data, to assess the advantages of data privacy against the possible drawbacks. Data from several sources must be combined for use in many data mining applications. The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures. Historically, numerous heuristics (e.g., greedy search) and metaheuristics-based techniques (e.g., evolutionary algorithm) have been created for the positive association rule in privacy preserving data mining (PPDM). When it comes to connecting seemingly unrelated diseases and drugs, negative association rules may be more informative than their positive counterparts. It is well-known that during negative association rules mining, a large number of uninteresting rules are formed, making this a difficult problem to tackle. In this research, we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users’ privacy. The applied approach dynamically determines the transactions to be interrupted for information hiding, as opposed to predefining them. This study introduces a novel method for addressing the problem of negative association rules in healthcare data mining, one that is based on the Tabu-genetic optimization paradigm. Tabu search is advantageous since it removes a huge number of unnecessary rules and item sets. Experiments using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions, as measured by the indicator of hiding failure.

Application of Data Mining in Network Information Dynamic Push Software

In order to solve the problem that it is difficult for each user to obtain the most needed information due to different backgrounds, a method of data mining network information is proposed. The main steps of this method are as follows: (1) designing data mining rules based on association rules; (2) screening candidate sets of network information data mining; (3) candidate set information data mining. The experimental results show that the p value of this method is more than 90.0% and that of the traditional method is only within the range of 30.0%∼70.0%. Obviously, the p value of this method is 20.0%∼60.0% higher than that of the traditional method. This method can effectively solve the problem that it is difficult for each user to obtain the most needed information due to background differences.

Research on the Relevance of Art Courses in Colleges and Universities Based on Data Mining

With the development of educational informatization, the educational data of each school is increasing day by day. How to rationally use the existing information to make scientific teaching decisions is a problem that every educator is closely concerned about. This paper proposes a correlation analysis method for college art courses based on data mining technology. Through the study of association rules in data mining, the Apriori algorithm based on three-dimensional matrix is used to quickly mine student performance data, so as to obtain some reasonable and reliable courses. The results show that the method in this paper can find valuable information for curriculum setting from the actual teaching data, so as to reasonably optimize the curriculum, provide a decision-making basis for the revision of the art curriculum and syllabus in colleges and universities, and further improve the teaching effect and the quality of personnel training.

URL Phishing Detection Using Particle Swarm Optimization and Data Mining

The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for research. Various techniques are adopted in the detection process, including neural networks, machine learning, or hybrid techniques. A novel detection model is proposed that uses data mining with the Particle Swarm Optimization technique (PSO) to increase and empower the method of detecting phishing URLs. Feature selection based on various techniques to identify the phishing candidates from the URL is conducted. In this approach, the features mined from the URL are extracted using data mining rules. The features are selected on the basis of URL structure. The classification of these features identified by the data mining rules is done using PSO techniques. The selection of features with PSO optimization makes it possible to identify phishing URLs. Using a large number of rule identifiers, the true positive rate for the identification of phishing URLs is maximized in this approach. The experiments show that feature selection using data mining and particle swarm optimization helps tremendously identify the phishing URLs based on the structure of the URL itself. Moreover, it can minimize processing time for identifying the phishing website instead. So, the approach can be beneficial to identify such URLs over the existing contemporary detecting models proposed before.

Geospatial Distribution of the Quality of the Coffee of Minas Gerais by Methods of Data Mining Techniques

The state of Minas Gerais is located in one of the most privileged Brazilian regions for the production of high-quality coffees. Due to its large territorial extension and environmental variation, it favors the production of specialty coffees with a great diversity of flavor and aroma. These differences are related to the peculiar characteristics of each microregion, particulary climate variations, altitude, latitude and production systems. Although the relationship between environmental characteristics and coffee productivity in different biomes is already well explored, its relationship with beverage quality still requires research. Given the need for more information on areas with potential for special coffee production, this paper proposes to associate data concerning to sensorial quality of coffees in the Cafés of Minas Quality Contest, with climatic and geographical variations of each state region, applying association rules for data mining, used to understand the aptitude of the state through the generation of rules that describe the most relevant patterns in the data. The climatic, geographical and classificatory information of the coffee samples is the result of the database of the contest participants in 2007 and 2008. The results demonstrate that with data mining techniques it was possible to associate the regions with the greatest potential with the climatic and geographical characteristics of the environment.

Fuzzy Comprehensive Evaluation Method of Ecological Environment Damage Compensation System and Management Based on Improved Association Rule Algorithm

In recent years, there have been frequent cases of damage to the ecological environment, and the public interests of members of society have been repeatedly violated. The safety of the ecological environment is worrying. This paper uses the association rule algorithm and fuzzy comprehensive evaluation method to improve the management of the ecological environment damage compensation system. This article has deeply studied the role of association rules in data mining and practically improved them and applied them to the management of the compensation system for environmental damage. This article also constructed a fuzzy comprehensive evaluation model, following the principles of innovation, responsibility, judicial, and openness, clarified the fuzzy comprehensive evaluation and evaluation process, established a set of comments on the evaluation object, determined the membership function, and constructed the damage factor and multiple index factors such as compensation factor and damage object. Based on the use of the improved correlation algorithm, the accuracy and complexity of the model have been improved, reaching 85.47 and 76.32 points, respectively. It is hoped that the research in this article can contribute a little to accelerating the establishment of my country’s ecological environment damage compensation system, safeguarding the interests of the ecological environment, and protecting the ecological environment.

Accurate Marketing Strategies Based On Data Analytics

In the current background of the times, China’s economic development is very rapid, all walks of life are changing with each passing day, so for marketing strategy, the current situation is also a challenge, but also an opportunity. Therefore, according to the current situation, this paper puts forward some methods of using data analysis to implement precision marketing. In this paper, on the premise of protecting user data security and not violating the data mining rules, in order to improve the accuracy of marketing, through comparing the current marketing model with the previous marketing model, and using literature research, questionnaire method, comparative analysis, mathematical statistics, a number of algorithms and other methods to get the experimental results. The experimental results show that the appropriate algorithm can make the marketing strategy get more accurate results.

Algorithm Study of New Association Rules and Classification Rules in Data Mining

In order to further improve the efficiency of data mining, it proposes a kind of data mining algorithm based on new association rules and classification rules. Specific research process is as follows. Firstly, MDML-PP algorithm is analyzed and applied to the multi-dimensional multi-level association rules in data mining, and then selects the test data sets for performance evaluation, meanwhile, it also studies the multi-support rate cut and classification rule mining algorithm, which is applied to multi-support rate classification rules in data mining, finally, using the compare experiment to verify the effectiveness of multi-support rate classification rules. The results show that the proposed algorithm in this paper can be better used in data mining with multi-dimensional and multi-layer association rules, which can improve the efficiency and quality of data mining.

Simple Modification for an Apriori Algorithm With Combination Reduction and Iteration Limitation Technique

Apriori algorithm is one of the methods with regard to association rules in data mining. This algorithm uses knowledge from an itemset previously formed with frequent occurrence frequencies to form the next itemset. An a priori algorithm generates a combination by iteration methods that are using repeated database scanning process, pairing one product with another product and then recording the number of occurrences of the combination with the minimum limit of support and confidence values. The a priori algorithm will slow down to an expanding database in the process of finding frequent itemset to form association rules. Modification techniques are needed to optimize the performance of a priori algorithms so as to get frequent itemset and to form association rules in a short time. Modifications in this study are obtained by using techniques combination reduction and iteration limitation. Testing is done by comparing the time and quality of the rules formed from the database scanning using a priori algorithms with and without modification. The results of the test show that the modified a priori algorithm tested with data samples of up to 500 transactions is proven to form rules faster with quality rules that are maintained.Keywords: Data Mining; Association Rules; Apriori Algorithms; Frequent Itemset; Apriori Modified;

A STUDY OF EXPERT SYSTEM FOR DETECTION OF VARIOUS DISEASES

In the field of medical regularly handles enormous amounts of data. Handling huge data by conventional methods can affect the results. Algorithms for machine learning can be used to find out facts in medical research, in particular for disease prediction. Expert System with artificial intelligence technology, Data mining technology has also been extensively developed, which has promoted the development of for detection and diagnosis of different diseases. The purpose this study aims to know the detection of brain, heart, kidney etc with the help of different algorithms such as Feedforward Backpropagation, Support Vector Machine, Generalized Regression Neural Network, Radial Basis Function and association rules in data mining. This study introduces the above neural network techniques in detail and experiments on medical data collected from hospital. The experimental results show that the some neural network techniques will detect the disease based on medical data collected from hospital and recommend medicine. Applying neural network techniques and data mining techniques based medical data disease detection has greatly improved the level of medical detections and understandings.

Penerapan Algoritma Apriori Untuk Menentukan Impor Barang Pada PT. Pafa Mandiri Sakti

In the world of international trade, export and import activities are very important activities especially for countries that have an open economy, such as our country, Indonesia. Where export is the activity of sending goods from abroad, while import is the activity of importing goods from abroad into the country. In Indonesia alone many companies are engaged in imports. As an importer company many obstacles are experienced, one of which is the accumulation of goods in the warehouse which causes losses for the company. This happens because of an imbalance between imported goods and market demand. Because of these problems, a method is needed to find out which products have the most sales and how the product is related to one another, one of the algorithms in the data mining algorithm is a priori algorithm. Apriori algorithms include types of association rules in data mining. One of the stages of association analysis that attracts many researchers to produce efficient algorithms is analysis of high frequency patterns (frequent pattern mining). The importance of an association can be known by two benchmarks, namely: support and confidence. Support (supporting value) is the percentage of the combination of these items in the database, while confidence (certainty value) is the strength of the relationship between items in the association rules. Apriori algorithms can help to develop an inventory strategy in order to facilitate marketing.

The Brazilian School Girls’ Perspectives on a Computer Science Major


 
 
 The field of Computer Science has been of little interest for girls straight out of high school, when considering undergraduate majors in Brazil. At the University of Bras ??lia’s Department of Computer Science, female students compose less than 10% of the student body. In an effort to understand the girls’ lack of interest in computer related courses, we applied an anonymous questionnaire, from 2011 to 2014, regarding their perceptions of the field. The participants were 3707 females students who completed an anonymous questionnaire. We applied Association Rules in Data Mining and Principal Component Analysis to analyze these responses . The knowledge gained through this study could guide future research on the matter and guidelines for motivating girls to pursue careers in Computer Science.
 
 

Application of Text Message Held in Image Using Combination of Least Significant Bit Method and One Time Pad

Stenography and security are one of the techniques to develop art in securing data. Stenography has the most important aspect is the level of security in data hiding, which makes the third party unable to detect some information that has been secured. Usually used to hide textinformationThe (LSB) algorithm is one of the basic algorithms proposed by Arawak and Giant in 1994 to determine the frequent item set for Boolean association rules. A priory algorithm includes the type of association rules in data mining. The rule that states associations between attributes are often called affinity analysis or market basket analysis. OTP can be widely used in business. With the knowledge of text message, concealment techniques will make it easier for companies to know the number of frequencies of sales data, making it easier for companies to take an appropriate transaction action. The results of this study, hide the text message on the image (image) by using a combination of LSB and Otp methods.