Employing rough sets and association rule mining in KANSEI knowledge extraction

With the increasing awareness of environmental protection and prominent problem of traditional energy, new energy vehicles are an important choice to replace traditional oil-fueled vehicles. As an important part of new energy vehicles, the design of front face has an important impact on vehicles’ image, sales, and brand awareness. A front face’s modeling design process of new energy vehicles is proposed in this paper based on Kansei Engineering (KE)/ Rough Set Theory (RST)/ Backpropagation Neural Network (BPNN). Firstly, Kansei semantic analysis is carried out on the front face’s modeling of new energy vehicles, including collecting the front face samples of new energy vehicles and Kansei semantic words, the collected Kansei semantic words are reduced dimensional and clustered by using Factor Analysis (FA). Secondly, the morphological analysis method is used to decompose the front face samples of new energy vehicles into different design features, the attribute reduction algorithm in RST is used to identify the key design features of new energy vehicles that have an important impact on users’ satisfaction. Finally, BPNN is used to establish the mapping model between the users’ Kansei image and the key design features of new energy vehicles’ front face, thus obtaining the optimal design combination of new energy vehicles’ front face with the highest Kansei value. The results enable designers to grasp users’ sentimental cognition of new energy vehicles’ front face modeling effectively and accurately so as to improve users’ purchase desire. This method can provide references for the modeling design of related products.

Intrusion Detection System has some defects, such as signatures being generated manually, updating attack signatures difficultly and doing nothing in front of ultra data set. This paper presents a hybrid approaches for modeling IDS. Association rule mining and Rough set theory are combined as a hierarchical hybrid intelligent system model. The hybrid intrusion detection model combines association rule mining and rough set theory to improve detection accuracy and reduce false alarm, unreal alarm and computational complexity. Empirical results illustrate that the hybrid intrusion detection model can detect intrusion more accurately.

 Abstract—Nowadays, the method of data mining, widely known as KDD (Knowledge-discovery in databases), is researched actively due to increasing amount of data. Decision tree, which is human-readable and understandable, is widely used in the field of rule extraction, data mining, and pattern recognition. Although decision tree is easy for quickly understanding the data, it has many faults in algorithm itself. In this paper, to compensate for these faults, we combined decision table (rough set theory) and ANN (Artificial Neural Network). With this combined algorithm, we could easily obtain more precise rules from iris data. Our experiment was in this order: 1) Find weights by using back propagation (bp) algorithm. (ANN). 2) Use dimensionality reduction method to delete huge amount of irrelevant data. (Rough set theory and decision table). 3) Make decision tree with pruned weights and rules. With ANN algorithm and decision table (dimensionality reduction method), we could easily notice the most influential (high entropy) character of iris data. With this algorithm, we could extract more precise and pruned rule from iris data sets.

Vector-based association rule discovery models have been provided to look for spatial knowledge in terrestrial applications recently. Most of them just consider the attribution relationship of local space or the topological relationship between parcels or objects. And they are difficult to consider the temporal change, distance relationship or direction relationship. In this presentation, an geoevent association rule discovery model (GEARDM) is developed, which is based on the spatiotemporal grid and rough set, and the experiment in marine fishery application is provided in support of the model. In GEARDM, the continuous spatiotemporal process is discretized by fuzzy preknowledge as spatiotemporal assignment and will be assigned into a decision table. The mining algorithm based on rough set results from the decision table in the geoevent association rules, which shows what kind of pattern of spatiotemporal assignment of environmental factors determine the happen or attribution of the geoevents. After the mined rules being analyzed by knowledge, the refined knowledge is obtained and the spatiotemporal assignment will be renewed. Then the flow of GEARDM feeds back to the phase of discretization and iterates until the final rules are reasonable. These knowledge-based rules also can be used in the expert system to predict the happen or the scale of geoevent as the experiments show.

It is very important to predict the reaction of customers to future products. Sample pictures and line sketches are commonly used for discovering consumers’ demands. However, these two agents are difficult to express the real product form clearly and comprehensively, which may affect the accuracy of consumer affection prediction. In this paper, a method based on Kansei engineering via virtual reality (VR) has been proposed to construct a mapping relationship between form elements and consumer affection from four dimensions of the overall, unit, interrelation, and detail (OUID), so as to provide references for product form design. The correlation between product form elements and the perceptual image was studied with multiple linear regression analysis and partial correlation analysis. The different effects of VR, sketches, and pictures on user affection prediction were compared in this paper as well. A hairdryer has been employed as an example to illustrate the proposed method. The results show that compared with sketch and picture, VR based-prediction model is more accurate and reliable in predicting users’ emotional preferences. In addition to the unit form, the overall features, interrelation, and details are also the key factors affecting consumer preference. Highlight Based on Kansei Engineering, this paper investigated the correlation between the form elements of the product and the user's affection via VR. The proposed approach can help designers to quickly and accurately capture the user's perceptual image and transform it into design elements. The different effects of VR, sketches, and pictures on customer affection prediction were compared. A decomposition model is proposed to analyse the product form elements on four dimensions (overall features, unit form, interrelation, and details).

Consumers’ affective responses (CARs) to the appearance of a product will greatly influence their purchasing decisions. In the product design field, researchers often provide adjectives so that consumers can express their subjective feelings. However, there exists both similarity and vagueness among these adjectives, which make it difficult to choose suitable adjectives for semantic differential (SD) experiments. This study proposes an approach based on factor analysis (FA), hierarchical clustering analysis (HCA) and k-means clustering. First, an SD experiments is conducted to collect CARs evaluated data about a series of selected product samples. Second, FA is applied to the CARs data to extract the factor loadings of the adjectives. Finally, the clustering approach, which comprises HCA and k-means clustering, is used to group these adjectives according to the factor loading results obtained from the FA. A case study of mobile phone design is used to demonstrate the effectiveness of the proposed approach. Three representative pairwise adjectives: coarse-delicate, unoriginal-creative and discordant-harmonious were obtained from the experimental results conducted on the initial set of 22 pairwise adjectives. This proposed method is very helpful to product designers during new product development. keywords:Factor analysis、Hierarchical cluster analysis、K-means clustering、Kansei engineering、Product design.

<p><strong>A common problem often encountered in sanitary napkin packaging is inadequate product protection. Failure to open or reseal the packaging and the use of materials that cannot be reused are complaints from 97% of consumers regarding sanitary napkin products currently on the market. This research aims to analyze packaging design elements according to consumer preferences. In this research, the Kansei Engineering and Rough Set methods were used. Kansei Engineering has the advantage of being able to translate consumer emotions into design elements. The design element analysis process is carried out using the Rough Set method. The research results obtained consumer emotions in the form of 19 Kansei words and 29 packaging samples as design references. Analysis of sanitary napkin packaging design elements obtained recommendations in the form of cardboard material, folding cardboard packaging type, no additional features, box body, opening type with flap, medium size, feminine design, script typography, and image elements in the form of pictures and illustrations. This research succeeded in proving Kansei Engineering's ability to produce packaging designs according to consumer preferences through quantitative data analysis<em>.</em></strong></p><p><strong><em>Keywords</em></strong> - <em>Model Kano, Rough Set, Design Element, Packaging</em></p>

The study of Influencing factors of traffic accidents is an important research direction in the field of traffic safety. In this paper, the traffic accidents of Shanghai Expressway from April to June 2014 were excavated using association rule mining which generated lots of frequent item sets. The strong rules hidden in these frequent item sets often uncover the association between influencing factors of accidents, which can be used to reduce the occurrence of accidents by breaking them. The rules can also be used to probe usual scenes of accidents, and some corresponding security improvement measures can be taken to prevent the accidents, and ultimately improve the city's traffic safety level. General speaking, association rule mining can produce tons of weak rules, the study first designed a method to calculate minimal Support value of training parameters, and further put forward a way to extract strong rules automatically. The results of the experiments showed that these methods proposed in the paper are effective. Therefore, an automatic modeling algorithm using association rules was finally established to promote the effective application of association rule mining on intelligent transportation system.

To reduce invalid rules in the mining of association rules, we have analyzed the reasons and presented a relative confidence in the judgment criteria. Based on the value of relative confidence, we classify strong association rules into positive, invalid and negative association rules. We offer an algorithm of mining association rules with new judgment criterion and make tests with Visual FoxPro. The tests indicate that the new method stated in this paper can obviously reduce invalid association rules.

This research demonstrates the application of association rule mining to spatio‐temporal data. Association rule mining seeks to discover associations among transactions encoded in a database. An association rule takes the form A→B where A (the antecedent) and B (the consequent) are sets of predicates. A spatio‐temporal association rule occurs when there is a spatio‐temporal relationship in the antecedent or consequent of the rule. As a case study, association rule mining is used to explore the spatial and temporal relationships among a set of variables that characterize socioeconomic and land cover change in the Denver, Colorado, USA region from 1970–1990. Geographic Information Systems (GIS)‐based data pre‐processing is used to integrate diverse data sets, extract spatio‐temporal relationships, classify numeric data into ordinal categories, and encode spatio‐temporal relationship data in tabular format for use by conventional (non‐spatio‐temporal) association rule mining software. Multiple level association rule mining is supported by the development of a hierarchical classification scheme (concept hierarchy) for each variable. Further research in spatio‐temporal association rule mining should address issues of data integration, data classification, the representation and calculation of spatial relationships, and strategies for finding ‘interesting’ rules.

A supermarket must have good business plan in order to meet customer desires. One way that can be done to meet customer desires is to find out the pattern of shopping purchases resulting from processing sales transaction data. Data processing produces information related to the function of the association between items of goods temporarily. Association rules functions in data mining.Association rule is one of the data mining techniques used to find patterns in combination of transaction data. Apriori algorithm can be used to find association rules. Apriori algorithm is used to find frequent itemset candidates who meet the support count. Frequent itemset that meets the support count is then processed using the temporal association rules method. The function of temporal association rules is as a time limitation in displaying the results of frequent itemsets and association rules. This study aims to produce rules from transaction data, apriori algorithm is used to form temporal association rules. The final results of this research are strong rules, they are rules that always appear in 3 years at certain time intervals with limitation on support and confidence, so that the rules can be used for business plan layout recommendations in Maharani Supermarket Demak.

Multi-dimensional semantic clustering of large databases for association rule mining

In this paper, we present a new framework for reusable association rule mining based on chi2 and odds ratio. We start at mining the association rules using standard Apriori algorithm. The strong rules are defined as association rules, while the weak rules will be evaluated by our proposed method. Firstly, the weak rules must be converted to 2 times 2 contingency table. We then compute the relationship between variables using chi2 and odds ratio. If the weak rules are related to each other with positive or negative relationship, then the weak rules will also be determined as association rules. Our system is evaluated with experiments on the crime data.

Rough sets and fuzzy sets are both theories for handling uncertain problems, and the viewpoints between them are different. They are complementary than competitive. Much seminal research has been carried out into combining them both; the results are rough fuzzy sets and fuzzy rough sets. In this paper, we introduce the idea of fuzzy rough sets in research of decision tables with quantitative data. Thus, we can handle the decision table directly without discretization. Combining the idea of variable precision rough sets, we make the form of the upper and lower approximation uniform.

Pharmacy sales transaction data contain valuable information on customer purchasing patterns; however, in practice, such data are often used merely as operational records, making relationships between purchased drugs difficult to identify. This study analyzes drug purchasing patterns using the Apriori and FP-Growth algorithms based on sales transaction data from Apotek Gadi Lamba Condet for the period January to June 2025. The transaction data were processed through data cleaning, drug name standardization, and transformation into transaction format, resulting in 7,038 transactions with 1,495 drug items. Association rule mining was performed using a minimum support of 0.01 and a minimum confidence of 0.17. The results show that the Apriori and FP-Growth algorithms generate ten identical association rules with the same support, confidence, and lift values, and all rules have lift values greater than one. Paracetamol 500 MG emerges as the most frequently involved drug in the association rules. These findings demonstrate that, for medium-scale pharmacy transaction datasets, Apriori and FP-Growth have equivalent capability in identifying drug purchasing patterns, with the primary difference lying in computational efficiency rather than the quality of the generated patterns.