International Journal of Data Mining & Knowledge Management Process最新文献

{"title":"FHCC: A SOFT HIERARCHICAL CLUSTERING APPROACH FOR COLLABORATIVE FILTERING RECOMMENDATION","authors":"Kaiman Zeng, Nansong Wu, Xiao-Kai Yang, Lu Wang, K. Yen","doi":"10.5121/IJDKP.2016.6303","DOIUrl":"https://doi.org/10.5121/IJDKP.2016.6303","url":null,"abstract":"Recommendation becomes a mainstream feature in nowadays e-commerce because of its significant contributions in promoting revenue and customer satisfaction. Given hundreds of millions of user activity logs and product items, accurate and efficient recommendation is a challenging computational task. This paper introduces a new soft hierarchical clustering algorithm - Fuzzy Hierarchical Co-clustering (FHCC) algorithm, and applies this algorithm to detect user-product joint groups from users’ behavior data for collaborative filtering recommendation. Via FHCC, complex relations among different data sources can be analyzed and understood comprehensively. Besides, FHCC is able to adapt to different types of applications according to the accessibility of data sources by carefully adjust the weights of different data sources. Experimental evaluations are performed on a benchmark rating dataset to extract user-product co-clusters. The results show that our proposed approach provide more meaningful recommendation results, and outperforms existing item-based and user-based collaborative filtering recommendations in terms of accuracy and ranked position.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128159777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UNDERSTANDING LEAST ABSOLUTE VALUE IN REGRESSION -BASED DATA MINING","authors":"Matt Wimble, M. Yoder, Young K. Ro","doi":"10.5121/IJDKP.2016.6301","DOIUrl":"https://doi.org/10.5121/IJDKP.2016.6301","url":null,"abstract":"This article advances our understanding of regression-based data mining by comparing the utility of Least Absolute Value (LAV) and Least Squares (LS) regression methods. Using demographic variables from U.S. state-wide data, we fit variable regression models to dependent variables of varying distributions using both LS and LAV. Forecasts generated from the resulting equations are used to compare the performance of the regression methods under different dependent variable distribution conditions. Initial findings indicate LAV procedures better forecast in data mining applications when the dependent variable is non-normal. Our results differ from those found in prior research using simulated data.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116672524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TWO LEVEL SELF -SUPERVISED RELATION EXTRACTION FROM MEDLINE USING UMLS","authors":"Huda Umar Banuqitah, F. Eassa, K. Jambi, M. Abulkhair","doi":"10.5121/IJDKP.2016.6302","DOIUrl":"https://doi.org/10.5121/IJDKP.2016.6302","url":null,"abstract":"The biomedical research literature is one among many other domains that hides a precious knowledge, and the biomedical community made an extensive use of this scientific literature to discover the facts of biomedical entities, such as disease, drugs,etc.MEDLINE is a huge database of biomedical research papers which remain a significantly underutilized source of biological information. Discovering the useful knowledge from such huge corpus leads to various problems related to the type of information such as the concepts related to the domain of texts and the semantic relationship associated with them. In this paper, we propose a Two-level model for Self-supervised relation extraction from MEDLINE using Unified Medical Language System (UMLS) Knowledge base. The model uses a Self-supervised Approach for Relation Extraction (RE) by constructing enhanced training examples using information from UMLS. The model shows a better result in comparison with current state of the art and naive approaches.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117158601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MCDM T ECHNIQUE TO EVALUATING MOBILE BANKING ADOPTION IN THE TOGOLESE BANKING INDUSTRY BASED ON THE PERCEIVED VALUE : P ERCEIVED BENEFIT AND PERCEIVED SACRIFICE FACTORS","authors":"Gbongli Komlan, Dumor Koffi, Kissi Mireku Kingsford","doi":"10.5121/IJDKP.2016.6304","DOIUrl":"https://doi.org/10.5121/IJDKP.2016.6304","url":null,"abstract":"Development of new technological innovations in networks, platforms, and financial institutions has empowered m-banking service providers to target their business strategies plan with confidence. Nevertheless, lots of service innovations have failed to generate revenue due to lagging adoption issues. Even though, several prior researches have focused on the factors that influence the acceptance of mbanking, there is limited empirical study which simultaneously take the success factors (perceived benefit) and resistance factors (perceived sacrifice) that contribute to the customers adoption of m- banking. Furthermore, most previous researches investigate on the overall aspects related to the adoption of mobile banking, have used multiple regression methods (structural equation modeling or technology acceptance technique) or some MCDM tools. However, these studies still have some shortcoming due to lack of delivering the enough information about these factors and their prioritizing facets. Nowadays, only those organizations which major goal is to achieve customer’s expectations with maximum quality can be successful in the market environment. This research reviewed the literature, took the advice from the experts in the field of m-banking service, and constructed the structural hierarchical table of the factors that affecting perceived value on consumers' adoption of mobile banking. Via a pairwise comparisons scale, a questionnaire designated was distributed to some university students in Lome-Togo and later used AHP (Analytic Hierarchy Process) to weigh each criteria, sub-criteria and alternative in order to rank them according to their preference. The finding revealed that financial risk is the utmost factor impacting the m-banking adoption, follow by money saved element. Both factors explain how consumers are more concerned with the financial or monetary issues and far ahead Security & Privacy risk come to confirm that, Togolese Banking industry should guarantee the safety and personal detail issues when customers are dealing with this m-banking technology. This research contributes to the existing literature by providing new insights to how AHP can be applied to assess consumer’s preference in m-banking in a developing country. Moreover, knowledge acquire from the influential factors will enable firms to better exploit their scarce resources, by formulating management strategies based on the priorities of these factors, and consequently, satisfy consumer needs and wants at lower cost while greater efficiency should be promoted.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114397034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A P ARTICLE SWARM OPTIMIZATION ALGORITHM BASED ON UNIFORM DESIGN","authors":"Adel H. Al-Mter, Songfeng Lu","doi":"10.5121/IJDKP.2016.6203","DOIUrl":"https://doi.org/10.5121/IJDKP.2016.6203","url":null,"abstract":"The Particle Swarm Optimization (PSO) Algorithm is one of swarm intelligence optimization algorithms. Usually the population’s values of PSO algorithm are random which leads to random distribution of search quality and search velocity. This paper presents PSO based on uniform design (UD). UD is widely used in various applications and introduced to generate an initial population, in which the population members are scattered uniformly over the search space. In evolution, UD is also introduced to replace some worse individuals. Based on abovementioned these technologies a Particle Swarm Optimization Algorithm based on Uniform Design (PSO-UD) algorithm is proposed. At last, the performance of PSOUD algorithm is tested and compared. Tests show that the PSO-UD algorithm faster than standard PSO algorithm with random populations.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132389387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AN ENHANCED FREQUENT PATTERN GROWTH BASED ON MAP REDUCE FOR MINING ASSOCIATION RULES","authors":"Arkan A. G. Al-Hamodi, Song Lu, Y. Alsalhi","doi":"10.5121/IJDKP.2016.6202","DOIUrl":"https://doi.org/10.5121/IJDKP.2016.6202","url":null,"abstract":"In mining frequent itemsets, one of most important algorithm is FP-growth. FP-growth proposes an algorithm to compress information needed for mining frequent itemsets in FP-tree and recursively constructs FP-trees to find all frequent itemsets. In this paper, we propose the EFP-growth (enhanced FPgrowth) algorithm to achieve the quality of FP-growth. Our proposed method implemented the EFPGrowth based on MapReduce framework using Hadoop approach. New method has high achieving performance compared with the basic FP-Growth. The EFP-growth it can work with the large datasets to discovery frequent patterns in a transaction database. Based on our method, the execution time under different minimum supports is decreased..","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128188741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ANOMALY DETECTION AND ATTRIBUTION USING AUTO FORECAST AND DIRECTED GRAPHS","authors":"V. Sankar, Somendra Tripathi","doi":"10.5121/IJDKP.2016.6205","DOIUrl":"https://doi.org/10.5121/IJDKP.2016.6205","url":null,"abstract":"In the business world, decision makers rely heavily on data to back their decisions. With the quantum of data increasing rapidly, traditional methods used to generate insights from reports and dashboards will soon become intractable. This creates a need for efficient systems which can substitute human intelligence and reduce time latency in decision making. This paper describes an approach to process time series data with multiple dimensions such as geographies, verticals, products, efficiently, and to detect anomalies in the data and further, to explain potential reasons for the occurrence of the anomalies. The algorithm implements auto selection of forecast models to make reliable forecasts and detect such anomalies. Depth First Search (DFS) is applied to analyse each of these anomalies and find its root causes. The algorithm filters the redundant causes and reports the insights to the stakeholders. Apart from being a hair-trigger KPI tracking mechanism, this algorithm can also be customized for problems lke A/B testing, campaign tracking and product evaluations.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123709181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A S URVEY OF LINK MINING AND ANOMALIES DETECTION","authors":"Zakea Idris Ali","doi":"10.5121/IJDKP.2016.6201","DOIUrl":"https://doi.org/10.5121/IJDKP.2016.6201","url":null,"abstract":"This survey introduces the emergence of link mining and its relevant application to detect anomalies which can include events that are unusual, out of the ordinary or rare, unexpected behaviour, or outliers.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130140359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Content Based Indexing of Music Objects Using Approximate Sequential Patterns","authors":"D. Vikram, M. Shashi","doi":"10.5121/IJDKP.2015.5207","DOIUrl":"https://doi.org/10.5121/IJDKP.2015.5207","url":null,"abstract":"The music objects are classified into Monophonic and Polyphonic. In Monophonic there is only one track which is the main melody that leads the song. In Polyphonic objects, there are several tracks that accompany the main melody. Each track is a sequence of notes played simultaneously with other tracks. But, the main melody captures the essence of the music and plays vital role in MIR. The MIR involves representation of main melody as a sequence of notes played, extraction of repeating patterns from it and matching of query sequence with frequent repeating sequential patterns constituting the music object. Repeating patterns are subsequences of notes played time and again in a main melody with possible variations in the notes to a tolerable extent. Similarly, the query sequence meant for retrieving a music object may not contain the repeating patterns of the main melody in its exact form. Hence, extraction of approximate patterns is essential for a MIR system. This paper proposes a novel method of finding approximate repeating patterns for the purpose of MIR. The effectiveness of methodology is tested and found satisfactory on real world data namely ‘Raga Surabhi’ an Indian Carnatic Music portal.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115538259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved Text Clustering with Neighbors","authors":"Y. SriLalitha, A. Govardhan","doi":"10.5121/IJDKP.2015.5203","DOIUrl":"https://doi.org/10.5121/IJDKP.2015.5203","url":null,"abstract":"","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121913017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}