Candidate Frequent Itemsets Research Articles

Efficient and accurate personalized product recommendations through frequent item set mining fusion algorithm

In the realm of personalized product recommendation, addressing the challenges of sparse data and “cold start” has been the primary focus. However, filtering invalid information amidst the overwhelming data on e-commerce platforms remains an underexplored issue. This paper proposes a fusion recommendation algorithm based on frequent item set mining to tackle this problem by compressing the commodity data set and identifying the frequent commodity set. The algorithm not only improves time efficiency by reducing the number of candidate frequent item sets but also generates more accurate recommendations by calculating user-commodity interest rankings and recommending similar products. We first present the existing problems in fusion recommendation algorithms based on frequent item set mining, such as redundant rules, low recommendation accuracy, and the inability to explore deep connections between users and products. Next, we introduce our proposed algorithm, which involves filtering the commodity data set, calculating user-commodity interest rankings, and defining similar product recommendation rules. The algorithm's effectiveness is demonstrated by its ability to adapt to users' dynamic preferences and capture their changing interests in real-time. A comparative analysis using our algorithm and other data mining algorithms reveals a reduction in the number of frequent commodity data sets and weighted frequent item sets, leading to decreased algorithm operation time. This research contributes to the development of more efficient and accurate personalized product recommendation algorithms, enhancing user experience on e-commerce platforms.

Semantic Trajectory Frequent Pattern Mining Model

A method for mining frequent patterns of individual user trajectories is proposed based on location semantics. The semantic trajectory is obtained by inverse geocoding and preprocessed to obtain the Top-k candidate frequent location item sets, and then the spatio-temporal sequence intersection and the divide and conquer merge methods are used to convert the frequent iterative calculation of long itemsets into hierarchical sets' regular operations, the superset and subset of frequent sequences are found. This kind of semantic trajectory frequent pattern mining can actively identify and discover potential carpooling needs, and provide higher accuracy for location-based intelligent recommendations such as carpooling and HOV lane travel (High-Occupancy Vehicle Lane). Carpool matching and recommendation based on semantic trajectory in this paper is suitable for single carpooling and relay-ride carpooling. the results of simulation carpooling experiments prove the applicability and efficiency of the method.

Analysis of Intelligent Energy Saving Strategy of 4G/5G Network Based on FP-Tree

With the large-scale deployment of 5G network of communication operators, there are more and more 5G devices, and the power consumption of mobile network surges. This paper uses the improved FP-Tree (Frequent Pattern Tree) to mine association rules for the service load of 4G/5G base stations of operators. It reduced the number of times of traversing the data set when generating candidate frequent and complex itemsets, and fully considers the problem of low utilization of equipment resources caused by ”Peak Valley Efect”, so as to improve the accuracy and operation efciency of the model. It greatly improves the energy-saving potential of the equipment. The evaluation of the operator’s 4G/5G network KPI, KQI and other indicators shows that the network performance is stable and the user perception is basically unchanged.

Status Quo of Numerical Simulation of Friction Stir Welding After Deformation under Big Data Analysis

Friction stir spot welding is a new type of connection technology developed on the basis of the friction stir welding process. It uses a high-speed rotating stirring head to generate heat by friction with the plate to make the welding material reach a thermoplastic softened state, forming a lap joint similar to resistance spot welding. The purpose of this article is to analyze and analyze the current situation of numerical simulation of friction stir welding after deformation under big data analysis. Algorithm mining commonly used for analysis of big data. This paper proposes to use the classic algorithm FP-growth algorithm. The FP-growth algorithm has the widest application range. It compresses the transaction database into a FP tree for processing. It also uses the Apriori algorithm, which eliminates the need to generate candidate frequent itemsets, which improves the efficiency of use. In addition, this article uses data mining technology and data fusion technology commonly used in big data analysis, and their application strengthens the analysis of the current situation of numerical simulation after friction stir welding deformation under big data analysis. Experimental research shows that the dynamic FSSW technology used in this paper simulates the numerical change after friction stir welding deformation, and the simulated data effect is better than the traditional GPC algorithm by about 20%.

Role of Computer Technology in the Work of College Party Branches in the Information Age

The work of party branch in colleges and universities is an important part of our party’s construction and an important part of building a harmonious campus. The purpose of this article is to research and analyze the role of computer technology in the work of college party branches in the information age. This paper proposes to use the classic algorithm FP-growth algorithm for digital technology problems commonly used in computer technology. The FP-growth algorithm has the widest application range. It compresses the transaction database into an FP tree for processing. It also uses the Apriori algorithm, which eliminates the need to generate candidate frequent itemsets, which improves the efficiency of use [1]. In addition, this article uses the two core technologies of computer technology, data mining technology and data fusion technology, and their use strengthens the research of computer technology in the work of college party branches in the information age. With the use of high-precision algorithm processing of computer technology, problems can be accurately found and solved in the work of the party branch. Through the high precision of the FP-growth algorithm and the Apriori algorithm, the computer can simulate the problems that will occur in the construction problem in the construction of college party branches and give solutions in advance [2]. The experimental results show that the application of computer technology in the work of party branches in colleges and universities in the information age explored in this article is more recognized and liked by party branch workers. The efficiency of party branch workers has increased by about 90%, which is helpful for improving party building efficiency. Played a significant role.

CL-MAX: a clustering-based approximation algorithm for mining maximal frequent itemsets

The problem of frequent itemset mining is one of the more important problems in data mining which has been extensively employed across a wide range of other relevant tasks such as market basket analysis in marketing, or text analysis in text mining applications. The majority of the deterministic frequent itemset mining algorithms which have been proposed in recent years use some sort or another of an optimal data structures to reduce the overall execution time of the algorithm. In this paper, however, we have tried instead to introduce an approximation algorithm which works by converting the problem into a clustering problem where similar transactions are grouped together. Each cluster centroid represents an itemset which may be assumed to be a candidate frequent itemsets. The validity of this assumption is simply verified by calculating the support count of these itemsets. Those who meet the min-support condition are considered to be an actual frequent itemset. As for the remaining itemsets, they are then passed to MAFIA which extract all maximal frequent itemsets therefrom. Experimentations made on several well-known and diverse datasets show that the proposed algorithm performs almost always faster, and in some cases up to 10 times faster, than the existing deterministic algorithms, and all this by retaining up to 95% of its accuracy.

Improved algorithm for parallel mining collaborative frequent itemsets in multiple data streams

With the rapid development of the World Wide Web technology, complex and diverse data present explosive growth, so frequent itemset mining plays an essential role. In view of the mining frequent itemsets in multiple data streams by limited computing power of a single processor, an improved algorithm of Parallel Mining Collaborative frequent itemsets in multiple data streams (PMCMD-Stream) was proposed. Firstly, the algorithm compresses the potential and frequent itemsets into CP-Tree only by one-scan and applies increment method to inserting or deleting related branch on CP-Tree, we do not need to repeatedly scanning the databases to generate many candidate frequent itemsets and save the running time. Secondly, this parallelized algorithm can be run in the MapReduce programming environment. Finally, the valuable frequent itemsets, namely global collaborative frequent itemsets, were obtained. Because each candidate frequent itemset is independent, and different candidate frequent itemsets can be processed by multiple computing machines concurrently. The experimental results show that PMCMD-Stream algorithm not only can improve the mining efficiency but also have much better scalability than the existing algorithms, so as to discover the collaborative frequent itemsets from large-scale data streams.

Research on Topology Association Rules Algorithm Based on Spatial Constraints

Spatial topological relation is an important and typical multilayer spatial relation, when Apriori is used to mining spatial constraint topology association rules, it will has some repeated computing. And so this paper proposes an algorithm of spatial constraint topology association rules mining based on complement set, which is used to mining spatial multilayer transverse association rules with constraint condition from spatial database. This algorithm generates candidate frequent topological itemsets with constraint condition not only by down-top search strategy as Apriori, but also by computing complement set of candidate from down-top search strategy, which is suitable for mining any spatial topological frequent itemsets with constraint condition. This algorithm compresses a kind of spatial topological relation to form an integer. By the way, firstly, the algorithm may efficiently reduce some storage space when creating mining database. Secondly, the algorithm is fast to obtain topological relation between two spatial objects, namely, it may easily compute support of candidate frequent itemsets. Finally, the algorithm may fast generate candidate via double search strategy, i.e. one is that it connects (k+1)-candidate frequent itemsets with constraint condition of k-frequent itemsets as down-top search strategy, the other is that it computes complement set of (k+1)-candidate frequent itemsets with constraint condition. The result of experiment indicates that the algorithm is able to extract spatial multilayer transverse association rules with constraint condition from spatial database via efficient data store, and it is very efficient to extract any frequent topology association rules with constraint condition.

Frequent Spatiotemporal Association Patterns Mining Based on Granular Computing

In order to discover multi-dimensional spatiotempor al association patterns, and improve the efficiency of traditional mining algorithms for spatiotemporal association patterns, this paper firstly construct s a star association model based on event, which can sh ow more spatiotemporal information on the basis of the present star association model. Besides traditi onal attributes association with point, line and pl ane, the model also can fully and flexibly express tempo ral association, orientation association, and topol ogy association, namely, it can quickly and simply form multi-dimensional spatiotemporal association patterns. And then for the star association model b ased on event, an algorithm of discovering frequent spatiotemporal association patterns based on granul ar computing is proposed, which is different from traditional association patterns mining algorithms. One is that the algorithm breaks traditional think ing of generating candidate frequent itemsets, namely, it generates candidate frequent itemsets by updatin g the mixed radix numeral. The method is quick and si mple to avoid redundant complicated calculations for adopting complex FP-tree data structure or gene rating candidate by joining frequent itemsets. The other is that the algorithm for discovering frequen t spatiotemporal association patterns only needs to read database once via granular computing, in other words, it discovers each frequent spatiotemporal association pattern via constructing a spatiotempor al information granule, where the intension can be mapped to the mixed radix numeral from the mixed ra dix notation system based on spatiotemporal information system. Finally, this paper further dis cusses the characteristics and the optimal applicat ion environments of the algorithm. Experimental results indicate that the algorithm is simpler and faster than these traditional frequent patterns mining alg orithms on the optimal application environments. Povzetek: Opisana je nova metoda za rudarjenje pros torsko-casovnih vzorcev.

Simulation System of Car Crash Test in C-NCAP Analysis Based on an Improved Apriori Algorithm*

Abstract In order to analysis car crash test in C-NCAP, an improved algorithm is given based on Apriori algorithm in this paper. The new algorithm is implemented with vertical data layout, breadth first searching, and intersecting. It takes advantage of the efficiency of vertical data layout and intersecting, and prunes candidate frequent item sets like Apriori. Finally, the new algorithm is applied in simulation of car crash test analysis system. The result shows that the relations will affect the C-NCAP test results, and it can provide a reference for the automotive design.

EVALUASI KINERJA ALGORITMA ASSOCIATION RULE

Association is a technique in data mining used to identify the relationship between itemsets in a database (association rule). Some researches in association rule since the invention of AIS algorithm in 1993 have yielded several new algorithms. Some of those used artificial datasets (IBM) and claimed by the authors to have a reliable performance in finding maximal frequent itemset. But these datasets have a different characteristics from real world dataset. The goal of this research is to compare the performance of Apriori and Cut Both Ways (CBW) algorithms using 3 real world datasets. We used small and large values of minimum support thresholds as atreatment for each algorithm and datasets. As a result we find that the characteristics of datasets have a signifcant effect on the performance of Apriori and CBW. Support counting strategy, horizontal counting, showed a better performance compared to vertical intersection although candidate frequent itemsets counted was fewer.

DHP 연관 규칙 탐사 알고리즘을 위한 효율적인 해싱 메카니즘

Apriori 알고리즘에 기반 한 연관 규칙 탐사 알고리즘들은 후보 빈발 항목 집합의 계수 관리를 위한 자료구조로 해시 트리를 사용하고, 많은 시간이 그 해시 트리를 검색하기 위해 소요된다. DHP 연관 규칙 탐사 알고리즘은 해시 트리에 대한 검색 시간을 절약하기 위해 검색 대상인 후보 빈발 항목 집합의 개수를 최대한 줄이고자 노력한다. 이를 위해 사전에 예비 후보 빈발 항목 집합에 대한 간편 계수를 실시한다. 이 때, 예비 계수에 필요한 계산 부담을 줄이기 위해 아주 간단한 직접 해시 테이블 사용을 권고한다. 이 논문에서는 DHP 연관 규칙 탐사 알고리즘의 단계 2에서 사전 전지를 위해 사용되는 직접 해시 테이블 <TEX>$H_2$</TEX>와 모든 단계에서 후보 빈발 항목 집합의 계수를 위해 사용되는 해시 트리 <TEX>$C_k$</TEX>에 적용될 수 있는 효율적인 해싱 메카니즘을 제안하고 검증한다. 검증 결과 일반적인 단순 제산(mod) 연산 방법을 사용했을 때보다 제안 방법을 적용했을 경우 최대 82.2%, 평균 18.5%의 성능 향상이 얻어지는 것으로 나타났다. Algorithms for mining association rules based on the Apriori algorithm use the hash tree data structure for storing and counting supports of the candidate frequent itemsets and the most part of the execution time is consumed for searching in the hash tree. The DHP(Direct Hashing and Pruning) algorithm makes efforts to reduce the number of the candidate frequent itemsets to save searching time in the hash tree. For this purpose, the DHP algorithm does preparative simple counting supports of the candidate frequent itemsets. At this time, the DHP algorithm uses the direct hash table to reduce the overhead of the preparative counting supports. This paper proposes and evaluates an efficient hashing mechanism for the direct hash table <TEX>$H_2$</TEX> which is for pruning in phase 2 and the hash tree <TEX>$C_k$</TEX>, which is for counting supports of the candidate frequent itemsets in all phases. The results showed that the performance improvement due to the proposed hashing mechanism was 82.2% on the maximum and 18.5% on the average compared to the conventional method using a simple mod operation.

A fast distributed algorithm for association rule mining based on binary coding mapping relation

Association rule mining is an important issue in data mining. The paper proposed an binary system based method to generate candidate frequent itemsets and corresponding supporting counts efficiently, which needs only some operations such as “and”, “or” and “xor”. Applying this idea in the existed distributed association rule mining algorithm FDM, the improved algorithm BFDM is proposed. The theoretical analysis and experiment testify that BFDM is effective and efficient.

A developed algorithm of apriori based on association analysis

A method for mining frequent itemsets by evaluating their probability of supports based on association analysis is presented. This paper obtains the probability of every 1-itemset by scanning the database, then evaluates the probability of every 2-itemset, every 3-itemset, everyk-itemset from the frequent 1-itemsets and gains all the candidate frequent itemsets. This paper also scans the database for verifying the support of the candidate frequent itemsets. Last, the frequent itemsets are mined. The method reduces a lot of time of scanning database and shortens the computation time of the algorithm.