Weighted Association Rule Mining Algorithm Research Articles

An Improved Recognition Method of Weighted Rules and Its Application in Recommendation Algorithm

The method proposed in this paper mainly uses the difference of the importance of items in the database to improve the problem of mining a large number of redundant and useless rules by traditional association rules mining algorithm. The method has made great improvement in three aspects of association rule design, which are effective length recognition of recommendation rule, weighted association rule mining combined with frequency analysis and time constraint application. The weighted temporal association rules mined by the effective frequency length weighted association rules mining algorithm can improve the accuracy of recommendation, and the accuracy of recommendation prediction is increased from 62% to 69%. The purpose of this algorithm is to mine hidden high value rules, and optimize the algorithm considering the time complexity of algorithm execution while ensuring the accuracy of the algorithm.

Exploiting weighted association rule mining for indicating synergic formation tactics in soccer teams

SummaryManagers make decisions on team tactics, formations, and player selection based on their own experiences. The managers have limitations in understanding the team's situation and sometimes they can think wrong. The purpose of this study is to make decisions on player selection and tactical formation according to the level of the opponent based on the data, not on the intuition of the manager. In our previous study, the Boruta algorithm was used to extract important features from 69 features in soccer player data by position. The detailed roles of each position were defined by using K‐means algorithm. For example, the detailed roles of each position were defined as Mezzala, Shadow Striker, Deep‐lying playmaker, and so on. That is, forward positions are classified as Target Man (TM) and Shadow Striker (SS). TM is a high‐goal, high‐competitive forward, and SS is a high‐dribble, high‐pass forward. In this study, we analyze a clustering dataset and the game appearance dataset. The game appearance dataset are divided into CL (Champions league Level), EL (Europa league Level), ML (Middle Level), and RL (Relegation Level). Association rule mining algorithm analyzes the synergy between positions, and selects a position with high synergy. Weighted association rule mining algorithm establishes player selection and tactical formation with the weight, which is the player's rating data. Finally, using the obtained results, we visualize the synergy between positions, tactical formation, and player characteristics depending on the level of the opponent.

A Methodology to Identify Multiple Equipment Coordinated Control with Power Metering System

Modern buildings are equipped with advanced Building Energy Monitoring System (BEMS) for energy supervision and management. To fully leverage the power of this system, a dynamic real time data analysis to understand the building operational strategies is critical. Considering the significant volume of building energy data, a computer aided technique is necessary for any this kind of analysis. However, until now, there still lacks an effective method to automatically identify building operational strategies. In this paper, a new methodology targeting at identify multiple equipment coordinated control is proposed, which adopts Symbolic Aggregation AproXimation (SAX) as data pre-processor and performs a weighted association rule mining algorithm (WARM) to identify the coordinated control strategy. Case studies show that the proposed framework can effectively identify schedule based operational strategies and detect abnormal energy use behavior.

Infrequent Weighted Item Set Mining in Complex Data Analysis

Infrequent Weighted Association Mining (IWAM) is one of the main areas in data mining for extracting the rare items in high dimensional datasets. Traditional Association rule mining algorithms produce large number of candidate sets along with the database scans. Due to large number of transactions and database size, traditional methods consume more time to find the relevant association rules with the specified threshold. Prior and post database scans are required an additional effort to validate the association rules. Most of the existing weighted models are implemented for mining frequent itemsets, but finding infrequent itemset mining are useful in many recent fields like web,medical,cloud,complex databases,protein sequence etc. In weighted infrequent association rule mining, each item in the transaction is assigned a weight in order to mine high utility infrequent itemsets. In this proposed work, weighted association rule mining algorithm is proposed to find infrequent itemsets using weighted threshold measures. Proposed approach gives better results on real-time datasets compare to existing weighted models.

A Hybrid Web Recommendation System Based on the Improved Association Rule Mining Algorithm

As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommendation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on association rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the frequency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages that are not yet visited by users is not included in the recommendation set. To overcome this problem, we have used the web usage log in the adaptive association rule based web mining where the association rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.

Weighted Association Rules Mining Algorithm Research

Aiming at the problem that most of weighted association rules mining algorithms have not the anti-monotonicity, this paper presents a weighted support-confidence framework which supports anti-monotonicity. On this basis, weighted boolean association rules mining algorithm and weighted fuzzy association rules mining algorithm are presented, which use pruning strategy of Apriori algorithm so that improve the efficiency of frequent itemsets generated. Experimental results show that both algorithms have good performance.

Effective page recommendation algorithms based on distributed learning automata and weighted association rules

Different efforts have been done to address the problem of information overload on the Internet. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests by extracting knowledge from the previous users’ interactions. In this paper, we propose three algorithms to solve the web page recommendation problem. In our first algorithm, we use distributed learning automata to learn the behavior of previous users’ and recommend pages to the current user based on learned patterns. By introducing a novel weighted association rule mining algorithm, we present our second algorithm for recommendation purpose. Also, a novel method is proposed to pure the current session window. One of the challenging problems in recommendation systems is dealing with unvisited or newly added pages. By considering this problem and improving the efficiency of first two algorithms we present a hybrid algorithm based on distributed learning automata and proposed weighted association rule mining algorithm. In the hybrid algorithm we employ the HITS algorithm to extend the recommendation set. Our experiments on real data set show that the hybrid algorithm performs better than the other algorithms we compared to and, at the same time, it is less complex than other proposed algorithms with respect to memory usage and computational cost too.