Equivalence Class Transformation Research Articles

Enhancing Marketing Strategies for Office Stationery Stores Using Equivalence Class Transformation Algorithms

Office stationery stores facilitate operations for offices, schools, and businesses. However, the store owner must overcome the challenge of selecting the right products for promotion. This research aims to tackle this issue by comparing the performance of the Equivalence Class Transformation algorithms in discovering association rules using Support, confidence, and lift ratios. The findings reveal that the algorithm generates association rules based on Support, confidence, and lift values. Ten critical rules are identified, shedding light on algorithm effectiveness. Ultimately, this study underscores the significance of refining marketing approaches for brick-and-mortar stationery businesses and the value of data-driven methods in aiding decision-making. In the early promotion catalog phase, priority is assigned to rules with solid agreement by the Shop Owner, guiding the selection of featured items based on robust product associations.

COMPARISON OF MARKET BASKET ANALYSIS METHOD USING APRIORI ALGORITHM, FREQUENT PATTERN GROWTH (FP- GROWTH) AND EQUIVALENCE CLASS TRANSFORMATION (ECLAT) (CASE STUDY: SUPERMARKET “X” TRANSACTION DATA FOR 2021)

The retail industry continues to grow and develop in Indonesia. The retail sector as a provider of goods used in everyday life has long started digital transformation in its business. Digital technology helps the retail industry collect valuable customer data. Business analytic is the use of data, information technology and statistical analysis, which is used to obtain information about a business and make decisions based on facts. Business analytic turns data into steps or actions in the context of making business decisions. Consumer needs and purchasing behavior can be predicted with big data-based technology. Association Rule is a technique in data mining to find the relationship between items in an item set combination. One of the utilizations of the association rule method is market basket analysis. Algorithms that can be used to analyze consumer purchasing patterns include the Apriori algorithm, Frequent Pattern Growth (FP-Growth) which represents a database structure in a horizontal format, and the Equivalence Class Transformation (ECLAT) algorithm which represents a vertical data format. In addition, this research will first analyze the complexity of the algorithm based on the time complexity in running the algorithm. This analysis uses these three algorithms, which are applied to Supermarket "X" transaction data in 2021, namely 136,202 transactions. The measure of goodness that is used to find out the best algorithm uses support and confidence values. The results show that the ECLAT algorithm is the most superior algorithm compared to the others based on the execution time required by the algorithm. The support value used in forming associations in the ECLAT algorithm is 1%, resulting in 19 rules. From the results of these rules, the highest support value was generated by the purchase of Indomie goreng special and Indomie ayam bawang, where as many as 1,362 shopping transactions bought these two items together or 2.71% of the total transactions.

Product Layout Determination System Using the Association Rules Method Using the Equivalence Class Transformation Algorithm

Competition in the business world, specifically in the sales industry, requires companies to analyze the purchases made by customers during transactions in order to find effective business strategies. In the competitive fashion industry, merchants devise marketing strategies to increase sales. One strategy that can attract consumer interest is by organizing and arranging product displays, placing them in perfect layouts that align with customers' buying habits, making it easier for them to find and purchase products. Layout arrangement significantly influences customer satisfaction and purchase intent. The algorithm used in this study is Equivalence Class Transformation (ECLAT). The data used consists of transactional data from Aufco Clothing, specifically fashion products. A total of 1041 transactions were analyzed, using variables such as order number and items sold. The data was processed using JavaScript, with a minimum support of 0.2 and a minimum confidence of 0.7, resulting in 16 rules. The rules ranged from a min. confidence of 70% to a maximum confidence of 100%, forming 6 rules with 9 combinations of items.

R-Diffset vs. IR-Diffset: Comparison Analysis in Dense and Sparse Data

The mining of concealed information from databases using Association Rule Mining seems to be promising. The successful extraction of this information will give a hand to many areas by aiding them in the process of finding solutions, economic projecting, commercialization policies, medical inspections, and numbers of other problems. ARM is the most outstanding method in the mining of remarkable related configurations from any groups of information. The important patterns encountered are categorized as recurrent/frequent and non-recurrent/infrequent. Most of the previous data mining methods concentrated on horizontal data set-ups. Nevertheless, recent studies have shown that vertical data formats are becoming the main concerns. One example of vertical data format is Rare Equivalence Class Transformation (R-Eclat). Due to its efficacy, R-Eclat algorithms have been commonly applied for the processing of large datasets. The R-Eclat algorithm is actually comprised of four types of variants. However, our work will only focus on the R-Diffset variant and Incremental R-Diffset (IR-Diffset). The performance analysis of the R-Diffset and IR-Diffset algorithms in the mining of sparse and dense data are compared. The processing time for R-Diffset algorithm, especially for sequential processing is very long. Thus, the incremental R-Diffset (IR-Diffset) has been established to solve this problem. While R-Diffset may only process the non-recurrent itemsets mining process in sequential form, IR-Diffset on the other hand has the capability to execute sequential data that have been fractionated. The advantages of this newly developed IR-Diffset may become a potential candidate in providing a time-efficient data mining process, especially those involving the large sets of data.

Product Recommendation System Application Using Web-Based Equivalence Class Transformation (Eclat) algorithm

The use of saved transaction data can provide a lot of knowledge that useful to the company in making policy and find the strategy in Alfamidi. In applying that goal, that is using Market Business Analysis. One of the techniques of Data Mining is Association Rule, which is the procedure of Market Basket Analysis to find the customer buying patterns. This pattern can be one of the ways in making policy and business strategy. One pattern determined by two parameters, they are support (support value) and confidence (certainly value). This analysis used algorithm Equivalence Class Transformation (ECLAT). One of the patterns resulted from analysis to the 30 transaction data with 12 category items. As an instance, if we buy strawberry jam then buy essence of bread with confidence value = 1%. The results obtained an also be used in helping the Alfamidi to help in determine the inventory decisions. So, the conclusion may be taken if consumers could buy strawberry jam then bought essence of bread simultaneously, then the Alfamidi should at least maintain the availability stock of both these items in order to remain the same.

A Theoretical Approach for Correlating Proteins to Malignant Diseases.

Malignant Tumors are developed over several years due to unknown biological factors. These biological factors induce changes in the body and consequently, they lead to Malignant Tumors. Some habits and behaviors initiate these biological factors. In effect, the immune system cannot recognize a Malignant Tumor as foreign tissue. In order to discover a fascinating pattern of these habits, behaviors, and diseases and to make effective decisions, different machine learning techniques should be used. This research attempts to find the association between normal proteins (environmental factors) and diseases that are difficult to diagnose and propose justifications for those diseases. This paper proposes a technique for medical data mining using association rules. The proposed technique overcomes some of the limitations in current association algorithms such as the Apriori algorithm and the Equivalence CLAss Transformation (ECLAT) algorithm. A modification to the Apriori algorithm has been proposed to mine Erythrocytes Dynamic Antigens Store (EDAS) data in a more efficient and tractable way. The experiments inferred that there is a relation between normal proteins as environment proteins, food proteins, commensal proteins, tissue proteins, and disease proteins. Also, the experiments show that habits and behaviors are associated with certain diseases. The presented tool can be used in clinical laboratories to discover the biological causes of malignant diseases.

MARKET BASKET ANALYST BASED ON WEBSITE USING ECLAT ALGORITHM (CASE STUDY POLA PHARMACY)

Market basket analyst is a data mining technique todiscover associations between datasets. Association rulemining identifies a relationship between a large set ofdata item. When a large quantity of data is constantlyobtained and stored in database, several industries arebecoming concerned about mining association rulesfrom their databases. Market basket analysis examinescustomer buying patterns by identifying associationsamong various items that customers place in theirshopping baskets. It is helpful to examine customerpurchasing behaviour and assists in increasing sales. Sothis system is intended to develop a system for marketbasket analysis on Pola Pharmacy which generateassociation rules among itemsets with the use of ECLAT(Equivalence Class Transformation) algorithm. Thissystem supports the decision making process for amarket expert.

Implementation of ECLAT Algorithm Technology: Determining Books Borrowing Pattern in University library

Library is one place to get lots of information. Every transaction information recorded in a fairly large database, the large data if it can’t be used it will make the problem for the librarian. This research aims to determine the books that are often borrowed when borrowing, using the rules of the mining association using the ECLAT algorithm. Equivalence Class Transformation Algorithm (ECLAT) performs frequent itemset search from the bottom. This algorithm will only scan the data once, the scanning process will not be repeated to get frequent k-itemset. The database scan process in the ECLAT algorithm is not repeated because on the seacrh itemset not pay attention the sequence from the item. The result of this study indicate the performance of the ECLAT algorithm is good by requiring 15 ms in the execution process. 102 transaction data the same minimum support is 1%, resulting in 21 frequent itemset.

I-Eclat: performance enhancement of eclat via incremental approach in frequent itemset mining

One example of the state-of-the-art vertical rule mining technique is called equivalence class transformation (Eclat) algorithm. Neither horizontal nor vertical data format, both are still suffering from the huge memory consumption. In response to the promising results of mining in a higher volume of data from a vertical format, and taking consideration of dynamic transaction of data in a database, the research proposes a performance enhancement of Eclat algorithm that relies on incremental approach called an Incremental-Eclat (i-Eclat) algorithm. Motivated from the fast intersection in Eclat, this algorithm of performance enhancement adopts via my structured query language (MySQL) database management system (DBMS) as its platform. It serves as the association rule mining database engine in testing benchmark frequent itemset mining (FIMI) datasets from online repository. The MySQL DBMS is chosen in order to reduce the preprocessing stages of datasets. The experimental results indicate that the proposed algorithm outperforms the traditional Eclat with 17% both in chess and T10I4D100K, 69% in mushroom, 5% and 8% in pumsb_star and retail datasets. Thus, among five (5) dense and sparse datasets, the average performance of i-Eclat is concluded to be 23% better than Eclat.

Smartphone customer segmentation based on the usage pattern

The dimension used to measure user heterogeneity plays a key role in the customer segmentation. For most traditional products, customer requirements (CRs) for products are often related to their basic characteristics and psychological characteristics. Therefore, in the traditional market segmentation theory, the dimensions used to distinguish customer differences include the demographic attributes such as gender, age, income etc., or the customer psychology. Customer behaviour, especially the purchasing behaviour, is also used as an important dimension for market segmentation. However, for the smart product like smartphone with rich functionality and multi-interactions, the customer’s interaction preference can be fully released. That makes the heterogeneity of customer stems from the usage characteristics rather than the traditional demographic attributes. Hence, the usage pattern is defined and proposed as the description of the usage characteristics and be used to measure the heterogeneity of customers in this research. The Equivalence CLAss Transformation (ECLAT) algorithm is employed to identify the customer’s APP frequent sets from the operating data and to construct the usage pattern. Thereafter, the customer can be segmented based on the distance among customers’ usage pattern. Compared with the demographic attributes, the usage pattern can provide more reliable and truthful measures for the smartphone customer segmentation.

A transformation algorithm for incompatible information systems based on variable precision values

The variable precision rough set (VPRS) model extends the basic rough set (RS) theory with finite universes and finite evaluative measures. VPRS is concerned with the equivalence and the contained relationship between two sets. In incompatible information systems, the inclusion degree and β upper (lower) approximation of the inconsistent equivalence class to the decision equivalence classes may be affected by the variable precision. The analysis of an example of incompatible decision table shows that there is a critical point in β available-values region. In the new β range limited at the critical point, the incompatible decision table can be converted to the coordination decision table reliably. The method and its algorithm implement are introduced for the critical value search. The examples of the inconsistent equivalence class transformation are exhibited. The results illustrate that this algorithm is rational and precise.

Observable Parameters of Fermion Mass Matrices in Multi-Higgs-Doublet Models

It is shown that, although the fermionic and scalar sectors of multi-Higgs-doublet models are not independent of each other, the fermion mass matrices, in particular, are invariant under the equiva­ lence class transformations of the scalar doublets. In light of this observation, a method originally formulated for counting the observable parameters of the Yukawa Lagrangian of the standard model is extended to count the observable parameters of the fermion mass matrices in multi-Higgs-doublet models. Completely different Yukawa coupling mass matrices may lead to the same set of physical observables.ll This is due to the invariance of the Lagrangian under the equivalence class transformations of a global group. In the standard model of electroweak interactions, one does not need to consider the Higgs potential when one determines this group. The subgroup of this group which preserves the forms of Yukawa coupling matrices, in turn, can be used to count the observable parameters of the Yukawa Lagrangian. 2 >.s> However, in the multi-Riggs-doublet extensions of the standard modeVH> one should consider the Higgs potential as well when one studies the Yukawa sector. This is because some of the parameters in the Higgs potential may be carried to the Yukawa sector by transforming the Higgs doublets under a unitary transformation and vice versa. Furthermore in these models, unlike in the standard model, the symmetries of the Y ukawa coupling matrices before and after the spontaneous symmetry breaking are not the same since the vacuum expecta­ tion values enter into the Yukawa coupling matrices after the spontaneous symmetry breaking as additional parameters. In this paper we study multi-Riggs-doublet models in the light of the above considerations. First we find that, although, in general, one must consider the sym­ metries of the Higgs potential as well when one determines the observable parameters of Yukawa Lagrangian, one does not need to consider the symmetries of the Higgs potential when one determines the observable parameters of the fermion mass matrices in particular since the fermion mass matrices are invariant under the equivalence class transformation of the scalar doublets. Then we examine how to find the global group which is used to count the observable parameters of the fermion mass matrices. We give some examples and a general analysis. One may write the Yukawa Lagrangian of a general k-Higgs-doublet version of the standard model as