Items In Transaction Research Articles

조선후기 한성부의 가옥 및 空垈 매매와 그 특징

This study aims to analyze the patterns and characteristics of house transactions in the Hanseong-bu area during the late Joseon. The main data that the investigator consulted came from the documents related to house transactions housed at Kyoto University of Japan. They are part of Korea's old documents taken out of the country to Japan during the colonial years. The focus was on the transaction records for houses and vacant lots within the five sections of Hanseong-bu between the middle 17th century and the middle 19th century. As for transaction methods, they showed a tendency of Gyeonggwan transactions(經官賣買) to have them notarized by the Hanseong-bu office until the middle 18th century. This characteristic was different from other areas where Baekmun transactions(白文賣買) completed only with a contract between the parties were common for land and houses with Gyeonggwan transactions generally implemented for servant transactions. As for the content of transactions, transaction sizes were about 50~60 rooms(間) in general. It was the most prominent that the same transaction items were traded consecutively at a short time cycle. Transactions were frequent. There were three cases in which it took less than a year to resell a house after its purchase. It took less than three years to resell in 50% of the cases. Gyeonggwan transactions and short transaction cycles were attributed to various causes including the shortage of hoses and lots and diverse ways of dwelling due to frequent population movement. Considering that the same phenomenon was found in land transactions in Hanseong-bu, there is a need to broaden the scope of finding the causes to the degree of urbanization or commercialization and differences in the perceptions of houses and land as one's property.

Enhancing the Performance of Association Rule Generation over Dynamic Data using Incremental Tree Structures

To discover a novel and dynamic approach for frequent itemsets generation and also for generating association rules is an imperative aspect in data mining. With the fast increase in databases, new transactions added, the incremental mining is acquainted to resolve the issues of maintaining association rules in updated databases. Earlier algorithms focused on this problem which consumed more time and costly to mine. This paper intends to analyze the tree construction like Frequent Pattern-tree(FP),PreOrderCoded(POC) tree and PrePostCoded(PPC) tree for sinking overheads and time constraints. To overcome theissueof updating association rules when new transactions addition this paper proposes a dynamic frequent itemsets mining approach using Incremental PreOrderCoded (IPOC)tree. This will reduce computational and scanning overheads of original dataset against addition of new transaction items and also works in an optimized way. An analysis was done on existing algorithms and compares time complexities for various standard datasets. The proposed approach shown better performance against existed ones over time and efficiency.

Improving Efficiency of High Utility Sequential Pattern Extraction

Text mining used on texts and publications in the biomedical and molecular biology fields is referred to as "biomedical text mining." It is a relatively new area of study at the intersection of computational linguistics, bioinformatics, and natural language processing. Superior usefulness the goal of sequential pattern mining is to identify statistically significant patterns among data instances when the values are presented sequentially. Time series mining is typically regarded as a distinct activity even if it is closely linked since it is typically assumed that the values are discrete. Structured data mining has a unique use known as sequential pattern mining. High utility pattern (HUP) mining is one of the most relevant study areas in data mining nowadays since it is capable of taking into consideration the nonbinary frequency values of items in transactions as well as different profit values for each item. The utilization of previous data structures as well as mining outcomes, yet, enables incremental and interactive data mining to eliminate the need for further calculations when a database is updated or the minimum threshold is modified. The method in this study suggests three innovative tree architectures for effective incremental and interactive HUP mining. The high utility sequential pattern mining issue has formalised key ideas and elements.

Purple empowerment --The political skills of Roman merchants over the Indian Ocean trade

Periplus leave numbers of the Roman empire trade records in the Indian Ocean. This paper mainly takes the transaction items of the purple clothes from Periplus as the entry point. Purple clothes are luxury goods with expensive value and hierarchical significance. The luxury purple clothing is usually sold along the Mediterranean coast but appears in the Indian Ocean market. Through the location and trade route of purple clothing in the Indian Ocean trading market, this paper reanalyzes the new diplomatic mode that the Roman Empire may carry out in the Indian Ocean, namely the local King obtains the authorization of the Roman Empire through purple clothes. If purple clothing is not only a luxury, but also a symbol of imperial power, then its presence in Indian Ocean trade will have political influence, not only as a diplomatic gift, but also as a potentially authorized item. It also reflects the different forms of expansion of the Roman Empire in the Indian Ocean.

PENENTUAN POLA SEKUENSIAL DATA TRANSAKSI PENJUALAN MENGGUNAKAN ALGORITMA SEQUENTIAL PATTREN DISCOVERY USING EQUIVALENT CLASSES (SPADE)

Transaction data is customer or customer data at a commercial or non-commercial institution that contains the consumer id, transaction time, and transaction items. From transaction data such as supermarket transactions, sequential patterns can be found to determine the interrelationship between items or items. Data if further processed or analyzed will produce information or knowledge that is important and valuable as a support in decision making. This study aims to determine the consumption patterns owned by customers and provide information that can be used in determining the layout of new store shelves. The SPADE algorithm is an algorithm for finding sequential patterns to break down the main problem into sub-problems that can be solved separately. Based on the result obtained it can be concluded that the application of the SPADE algorithm has the highest minimum support value that can still form maximal frequent sequences is 29%. The highest minimum support value of the SPADE algorithm is 0.2% with a maximum minimum confidence value of 81% and the number of rules formed is 1,118 Rule, but confidence is taken 60% up so that there are only 15 Rule. Whereas the Apriori algorithm has the highest minimum support value that can still form the maximum frequent sequences is 25%. The highest minimum support value of the apriori algorithm which can still form a rule is 0.3% with a maximum value of 88% minimum confidence and the number of rules formed as many as 494 Rule, but confidence is taken 60% up so that there are only 29 Rule.

HUOPM: High-Utility Occupancy Pattern Mining.

Mining useful patterns from varied types of databases is an important research topic, which has many real-life applications. Most studies have considered the frequency as sole interestingness measure to identify high-quality patterns. However, each object is different in nature. The relative importance of objects is not equal, in terms of criteria, such as the utility, risk, or interest. Besides, another limitation of frequent patterns is that they generally have a low occupancy, that is, they often represent small sets of items in transactions containing many items and, thus, may not be truly representative of these transactions. To extract high-quality patterns in real-life applications, this paper extends the occupancy measure to also assess the utility of patterns in transaction databases. We propose an efficient algorithm named high-utility occupancy pattern mining (HUOPM). It considers user preferences in terms of frequency, utility, and occupancy. A novel frequency-utility tree and two compact data structures, called the utility-occupancy list and frequency-utility table, are designed to provide global and partial downward closure properties for pruning the search space. The proposed method can efficiently discover the complete set of high-quality patterns without candidate generation. Extensive experiments have been conducted on several datasets to evaluate the effectiveness and efficiency of the proposed algorithm. Results show that the derived patterns are intelligible, reasonable, and acceptable, and that HUOPM with its pruning strategies outperforms the state-of-the-art algorithm, in terms of runtime and search space, respectively.

Determinants of Mobile Banking Usage or Transaction Frequency: Evidence from Selected Branches of Commercial Bank of Ethiopia (CBE) in Addis Ababa, Ethiopia

<p>This study considered data collected from 150 customers of Commercial Bank of Ethiopia(CBE) in Addis Ababa, Ethiopia. Those respondents were selected randomly from five branches of CBE in Addis Ababa: Finfinne branch, Theodros branch, Arat Killo branch, Saris Branch, and TemenjaYazh Branch. In drawing the samples from those representative branches, non-probability convenience sampling method has been used where customers are intercepted at the branches of the banks until the required sample size is reached.</p><p>The objectives of this study are to assess: mobile banking usage frequency of customers, mobile banking transaction items, customers’ perceived benefit of mobile banking, compelling factors to use mobile banking, perception and satisfaction level of customers.</p><p>Accordingly, of the distributed 150 questionnaires 113 were returned and valid for analysis. Based on this data the following are some of conclusions were made:</p><p>This study unveils that interruption of network and uneasiness to login are the major problems of mobile banking service provision in the study area.</p><p>From the ologit analysis result the study confirms usage frequency of mobile banking service is significantly affected by Age of the user (subscriber for ATM services), gender (subscriber for ATM services), and network interruption, lack of security, login difficulty, service charge and Doubt of the user about security. However, the availability of Forex information in the mobile banking service package does not have significant relationship with usage frequency. Specifically age of the user, logindifficulty, service charge and network interruption have extremely strong relationship with the usage frequency of customers towards mobile banking services.</p>

Large-scale frequent stem pattern mining in RNA families

Functionally similar non-coding RNAs are expected to be similar in certain regions of their secondary structures. These similar regions are called common structure motifs, and are structurally conserved throughout evolution to maintain their functional roles. Common structure motif identification is one of the critical tasks in RNA secondary structure analysis. Nevertheless, current approaches suffer several limitations, and/or do not scale with both structure size and the number of input secondary structures. In this work, we present a method to transform the conserved base pair stems into transaction items and apply frequent itemset mining to identify common structure motifs existing in a majority of input structures. Our experimental results on telomerase and ribosomal RNA secondary structures report frequent stem patterns that are of biological significance. Moreover, the algorithms utilized in our method are scalable and frequent stem patterns can be identified efficiently among many large structures.

Postdiffset: an Eclat-like algorithm for frequent itemset mining

Frequent itemset mining is a major field in data mining techniques. This is because it deals with usual and normal occurrences of set of items in a database transaction. Originated from market basket analysis, frequent itemset generation may lead to the formulation of association rule as to derive correlation or patterns. Association rule mining still remains as one of the most prominent areas in data mining that aims to extract interesting correlations, frequent patterns, association or casual structures among set of items in the transaction databases. Underlying structure of association rules mining algorithms are based upon horizontal or vertical data formats. These two data formats have been widely discussed by showing few examples of algorithm of each data formats. The works on horizontal approaches suffer in many candidate generation and multiple database scans that contributes to higher memory consumptions. In response to improve on horizontal approach, the works on vertical approaches are established. Eclat algorithm is one example of algorithm in vertical approach database format. Motivated to its ‘fast intersection’, in this paper, we review and analyze the fundamental Eclat and Eclat-variants such as tidset, diffset, and sortdiffset. In response to vertical data format and as a continuity to Eclat extension, we propose a postdiffset algorithm as a new member in Eclat variants that use tidset format in the first looping and diffset in the later looping. We present the performance of postdiffset results in time execution as to indicate some improvements has been achieved in frequent itemset mining.

Attention-Based Transactional Context Embedding for Next-Item Recommendation

To recommend the next item to a user in a transactional context is practical yet challenging in applications such as marketing campaigns. Transactional context refers to the items that are observable in a transaction. Most existing transaction based recommender systems (TBRSs) make recommendations by mainly considering recently occurring items instead of all the ones observed in the current context. Moreover, they often assume a rigid order between items within a transaction, which is not always practical. More importantly, a long transaction often contains many items irreverent to the next choice, which tends to overwhelm the influence of a few truly relevant ones. Therefore, we posit that a good TBRS should not only consider all the observed items in the current transaction but also weight them with different relevance to build an attentive context that outputs the proper next item with a high probability. To this end, we design an effective attention based transaction embedding model (ATEM) for context embedding to weight each observed item in a transaction without assuming order. The empirical study on real-world transaction datasets proves that ATEM significantly outperforms the state-of-the-art methods in terms of both accuracy and novelty.

Adaptive-Miner: an efficient distributed association rule mining algorithm on Spark

Extraction of valuable data from extensive datasets is a standout amongst the most vital exploration issues. Association rule mining is one of the highly used methods for this purpose. Finding possible associations between items in large transaction based datasets (finding frequent itemsets) is most crucial part of the association rule mining task. Many single-machine based association rule mining algorithms exist but the massive amount of data available these days is above the capacity of a single machine based algorithm. Therefore, to meet the demands of this ever-growing enormous data, there is a need for distributed association rule mining algorithm which can run on multiple machines. For these types of parallel/distributed applications, MapReduce is one of the best fault-tolerant frameworks. Hadoop is one of the most popular open-source software frameworks with MapReduce based approach for distributed storage and processing of large datasets using standalone clusters built from commodity hardware. But heavy disk I/O operation at each iteration of a highly iterative algorithm like Apriori makes Hadoop inefficient. A number of MapReduce based platforms are being developed for parallel computing in recent years. Among them, a platform, namely, Spark have attracted a lot of attention because of its inbuilt support to distributed computations. Therefore, we implemented a distributed association rule mining algorithm on Spark named as Adaptive-Miner which uses adaptive approach for finding frequent patterns with higher accuracy and efficiency. Adaptive-Miner uses an adaptive strategy based on the partial processing of datasets. Adaptive-Miner makes execution plans before every iteration and goes with the best suitable plan to minimize time and space complexity. Adpative-Miner is a dynamic association rule mining algorithm which change its approach based on the nature of dataset. Therefore, it is different and better than state-of-the-art static association rule mining algorithms. We conduct in-depth experiments to gain insight into the effectiveness, efficiency, and scalability of the Adaptive-Miner algorithm on Spark. Available: https://github.com/sanjaysinghrathi/Adaptive-Miner

Mining Negatives Association Rules Using Constraints

Pattern discovery techniques, such as association rule discovery is one of the fundamental problem in data mining. Usually the task is limited to positive rules of the form X → Y when X and Y are susbets of items. To enlarge the knowledge discovery from data. Many works pointed out that other rules can be mined linking the present items in transactions with the missing ones designed as negative rules. To mine the most relevant negative rules, the mining task of negative association rules is often coupled with new measure such as lift or conviction to limit the set of extracted association rules.In this work we address the problem of mining strong negative rules by extending the SAT-Based approach proposed in [1]. We show that the conviction constraint leads to a non-linear constraints that have to be managed efficiently to prune the search space. Experiments results explore the efficiency of our new approach.

Application of Incremental Updating Association Mining Algorithm in Geological Disasters System

Abstract Aiming at the problems of low efficiency, low cost of time and space, This paper proposes an algorithm to update the association mining of the inverted index tree. The algorithm combines the inverted index technology with the tree structure. When the data in the database is continuously updated, it can scan only the newly added part of the database, without having to scan the original database to count the number of transaction items. The optimal threshold predicted by Newton’s interpolation formula is compared with this frequency to get frequent item sets. Then, the confidence level is calculated for the combinations of different item sets in frequent item sets, and the correlation rules are obtained, and the correlation analysis of the rules is carried out to obtain a more realistic association rule. The inverted index tree updating association mining algorithm was applied to the data analysis of geological hazards monitoring system. One year data record of rainfall, groundwater level, soil water content and topography data was selected as the experimental data set. Compared with the IUAR algorithm, it is found that the inverted index tree updating association mining algorithm has some improvements in memory consumption and efficiency. The experimental results show that When the minimum support of IUAR algorithm remains unchanged, the number of transaction records is the same as the amount of new data, and Inverted Index Tree Incremental Updating Association Mining Algorithm takes less than 2/5 of the IUAR algorithm. When the number of transaction records and the amount of new data remain unchanged and IUAR algorithm support changes, the Inverted Index Tree Incremental Updating Association Mining Algorithm memory consumption is much smaller than IUAR algorithm. In the process of experiment, according to the results of the Inverted Index Tree Incremental Updating Association Mining Algorithm, the association rules are obtained and the correlation is judged. The strong association rules are used to set the alarm threshold of the geological disaster monitoring system.

OOIMASP: Origin based association rule mining with order independent mostly associated sequential patterns

Efficient mining of association rules on a transaction dataset is an interesting and a challenging problem. The state-of-the-art MASP algorithm is dependent on the order of items in the transaction. We propose OOIMASP algorithm, which has two novel properties- 1) order independence and 2) it takes into consideration the origin of items to calculate unbiased support and unbiased confidence values. Order dependence is one of the drawbacks of MASP. OOIMASP addresses this issue by rearranging the items in transactions using a greedy frequency based approach. We compare the performance of our system with MASP on five synthetic data sets and three public data sets. The results show that our proposed approach outperforms the MASP in both the comparison metrics, i.e., the number of association rules generated and the length of the longest association rule. Both these metrics are important to evaluate the performance of an algorithm. On an average, OOIMASP algorithm generates 632% longer rules and 457% more association rules than MASP algorithm. The disadvantage of the proposed algorithm is, it requires more computational resources in terms of time, approximately 5 times more than MASP. We claim that the extra information extracted using our method compensates for the increase in time complexity as compared to MASP. The proposed method produces multiple trees which can be very useful in the visual analysis of data.

Http://journal.uii.ac.id/index.php/Eksakta/article/view/7454

In this research apriori algorithm was implemented on rawi data of hadits to find out pattern of relationship among the rawi hadits in shahih bukhori book. The analogy that can be compared is by assuming that series or chain of rawi in every sanad of hadis may be regarded as transaction, while rawi hadis were items in transaction. Data mining is the method that was used to analyze with association rule technique. Association rule technique is used to find a pattern rule between a combination of items. To find out association rule by using association rule application, it is used apriori algorithm by observing three important measurement, namely support, confidence, and lift values. The analysis result of apriori algorithm showed that for minimum support 0.03 and minimum confidence 0.9 having 9 strong association pattern based on the sequence of rawi hadis of its sanad. Every rule of association which was strong, then tested by chi-square to prove that the rawis that were in the rule were truly connected or statisticly significance

EHAUPM: Efficient High Average-Utility Pattern Mining With Tighter Upper Bounds

High-utility itemset mining (HUIM) has become a popular data mining task, as it can reveal patterns that have a high-utility, contrarily to frequent pattern mining, which focuses on discovering frequent patterns. High average-utility itemset mining (HAUIM) is a variation of HUIM that provides an alternative measure, called the average utility, to select patterns by considering both their utilities and lengths. In the last decades, several algorithms have been developed to mine high average-utility itemsets (HAUIs). But most of them consume large amounts of memory and have long execution times, since they generally utilize the average-utility upper-bound ( auub ) model to overestimate the average utilities of itemsets. To improve the performance of HAUIM, this paper proposes two novel tighter upper-bound models as alternative to the traditional auub model for miningHAUIs. The looser upper-bound model considers the remaining-maximum utility in transactions to reduce the upper bound on the utilities of itemsets. The second upper-bound model ignores irrelevant items in transactions to further tighten the upper bound. Three pruning strategies are also designed to reduce the search space for mining HAUIs by a greater amount compared with the state-of-the-art HAUI-Miner algorithm. Experiments conducted on several benchmark data sets show that the designed algorithm integrating the two novel upper-bound models outperforms the traditional HAUI-Miner algorithm in terms of runtime, memory usage, number of join operations, and scalability.

Implementation of Al-Wadiah (saving instrument) Contract In Contemporary Gold Transaction

<p>Contemporary gold trading is like other retails and online trading. However, according to Islamic business contract, gold is categorized under a special law of transaction items and it must follows a specific law of Islamic business transaction. This article focuses on how wadiah concepts can be applied in the contemporary gold trading. The legality of the wadiah concepts is utmost important to be verified before its implementation. The aims of this article is to substantiate the concepts of wadiah in contemporary gold trading based on the main sources of Islamic law transactions. The finding shows that the wadiah concept is permissible in Islam. Furthermore, the wadiah also play important role in contemporary gold trading. Hence, wadiah contract can helps to resolve the gold trading issues where the physical assets of gold are distance from the buyer.</p>

Discovering Weighted Calendar-Based Temporal Relationship Rules using Frequent Pattern Tree

The advent of data mining approach has brought many fascinating situations and several challenges to database community. The objective of data mining is to explore the unseen patterns in data, which are valid, novel, potentially subsidiary and ultimately understandable. The authorize and real-time transactional databases often show temporal feature and time validity life-span. Utilizing temporal relationship rule mining one may determine unusual relationship rules regarding different time-intervals. Some relationship rules may hold, through some intervals while not others and this may lead to subsidiary information. Using calendar mined patterns has already been projected by researchers to confine the time-validity relationships. However, when we consider the weight factor like utility of item in transactions and if we incorporate this weight factor in our model to mine then fascinating results of relationships come on time–variant data. This manuscript propose a narrative procedure to find relationship rule on time-variant-weighted data utilizing frequent pattern tree-hierarchical structures which give us a consequential benefit in expressions of time and memory-recollection utilization though including time and weight factor.

Efficient Mining of Profit Rules from Closed Inter-Transaction Itemsets

Data mining applications in financial sectors are very common since investors can apply the resultant rules to make profits. Profit mining algorithms in particular, such as PRMiner, can generate profit rules that meet the expectations of investors regarding profit, risk, and win rate. However, most of such algorithms are not efficient due to the long processing time involving going through the whole search space in complex dynamical systems of financial markets. Hence, we propose a new approach in this paper to solve the problem by using closed itemsets to obtain profit rules without processing the entire trading rules. Based on the inter-day modeling, we analyze inter-transactions and conduct trading simulations to predict trading results for efficient profit rule generation. We develop two algorithms of JCMiner and ATMiner to process closed itemsets, which have better performance than the approach of PRMiner, especially for the large number of itemsets and large datasets. According to the experimental results, our algorithms outperform PRMiner in various experimental scenarios, i.e., mining parameters, the number of items in a transaction, and the number of transactions in a dataset.

Boosting association rule mining in large datasets via Gibbs sampling

Current algorithms for association rule mining from transaction data are mostly deterministic and enumerative. They can be computationally intractable even for mining a dataset containing just a few hundred transaction items, if no action is taken to constrain the search space. In this paper, we develop a Gibbs-sampling-induced stochastic search procedure to randomly sample association rules from the itemset space, and perform rule mining from the reduced transaction dataset generated by the sample. Also a general rule importance measure is proposed to direct the stochastic search so that, as a result of the randomly generated association rules constituting an ergodic Markov chain, the overall most important rules in the itemset space can be uncovered from the reduced dataset with probability 1 in the limit. In the simulation study and a real genomic data example, we show how to boost association rule mining by an integrated use of the stochastic search and the Apriori algorithm.