The increasing demand of world wide web raises the need of predicting the user's web page request.The most widely used approach to predict the web pages is the pattern discovery process of Web usage mining. This process involves inevitability of many techniques like Markov model, association rules and clustering. Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague set theory.
Today most of the information in all areas is available over the web. It increases the web utilization as well as attracts the interest of researchers to improve the effectiveness of web access and web utilization. As the number of web clients gets increased, the bandwidth sharing is performed that decreases the web access efficiency. Web page prefetching improves the effectiveness of web access by availing the next required web page before the user demand. It is an intelligent predictive mining that analyze the user web access history and predict the next page. In this work, vague improved ma…
Association rules express implication formed relations among attributes in databases of itemsets. The apriori algorithm is presented, the basis for most association rule mining algorithms. It works by pruning away rules that need not be evaluated based on the user specified minimum support confidence. Additionally, variations of the algorithm are presented that enable it to handle quantitative attributes and to extract rules about generalizations of items, but preserve the downward closure property that enables pruning. Intertransformation of the extensions is proposed for special cases.
Granular association rule mining is a new relational data mining approach to reveal patterns hidden in multiple tables. The current research of granular association rule mining considers only nominal data. In this paper, we study the impact of discretization approaches on mining semantically richer and stronger rules from numeric data. Specifically, the Equal Width approach and the Equal Frequency approach are adopted and compared. The setting of interval numbers is a key issue in discretization approaches, so we compare different settings through experiments on a well-known real life data set…
Exponential growth of the web increased the importance of web document classification and data mining. To get the exact information, in the form of knowing what classes a web document belongs to, is expensive. Automatic classification of web document is of great use to search engines which provides this information at a low cost. In this paper, we propose an approach for classifying the web document using the frequent item word sets generated by the Frequent Pattern (FP) Growth which is an association analysis technique of data mining. These set of associated words act as feature set. The fina…
Multidimensional association rule mining searches for interesting relationship among the values from different dimensions or attributes in a relational database. In this method the correlation is among set of dimensions i.e., the items forming a rule come from different dimensions. Therefore each dimension should be partitioned at the fuzzy set level. This paper proposes a new algorithm for generating multidimensional association rules by utilizing fuzzy sets. A database consisting of fuzzy transactions, the Apriory property is employed to prune the useless candidates, itemsets.
This study aims to explore the associations between near-crash events and road geometry and trip features by investigating a naturalistic driving dataset and a corresponding roadway inventory dataset using an association rule mining method.
Recommender systems are important for e-commerce companies as well as researchers. Recently, granular association rules have been proposed for cold-start recommendation. However, existing approaches reserve only globally strong rules; therefore some users may receive no recommendation at all. In this paper, we propose to mine the top-k granular association rules for each user. First we define three measures of granular association rules. These are the source coverage which measures the user granule size, the target coverage which measures the item granule size, and the confidence which measure…
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 recommen-dation 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 …
Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Events
Arizona State University（亚利桑那州立大学）；Alaska Native Tribal Health Consortium（阿拉斯加原住民部落健康联盟）；Bentley University（本特利大学）；Woodwell Climate Research Center（伍德韦尔气候研究中心）
Stamford University Bangladesh（孟加拉国斯坦福德大学）；International Islamic University Chittagong（吉大港国际伊斯兰大学）；Bangladesh University of Engineering & Technology（孟加拉工程技术大学）
As the amount of online text increases, the demand for text classification to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Automatic classification of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. In this paper we will discuss a procedure of classifying text using the concept of association rule of data mining. Association rule mining techniq…
We present a simple text mining method that is easy to implement, requires minimal data collection and preparation, and is easy to use for proposing ranked associations between a list of target terms and a key phrase. We call this method KinderMiner, and apply it to two biomedical applications. The first application is to identify relevant transcription factors for cell reprogramming, and the second is to identify potential drugs for investigation in drug repositioning. We compare the results from our algorithm to existing data and state-of-the-art algorithms, demonstrating compelling results …
Granular association rules reveal patterns hide in many-to-many relationships which are common in relational databases. In recommender systems, these rules are appropriate for cold start recommendation, where a customer or a product has just entered the system. An example of such rules might be "40% men like at least 30% kinds of alcohol; 45% customers are men and 6% products are alcohol." Mining such rules is a challenging problem due to pattern explosion. In this paper, we propose a new type of parametric rough sets on two universes to study this problem. The model is deliberately defined su…
The greatest challenge in maximizing the use of gene expression data is to develop new computational tools capable of interconnecting and interpreting the results from different organisms and experimental settings. We propose an integrative and comprehensive approach including a super-chip containing data from microarray experiments collected on different species subjected to hypoxic and anoxic stress. A data mining technology called Peano count tree (P-trees) is used to represent genomic data in multidimensions. Each microarray spot is presented as a pixel with its corresponding red/green int…
Data association methods are used by autonomous robots to find matches between the current landmarks and the new set of observed features. We seek a framework for opinion mining to benefit from advancements in autonomous robot navigation in both research and development
K. S. Rangasamy College of Technology（K.S.拉加斯瓦米技术学院）；Muthayammal Engineering College（穆塔亚马尔工程学院）
An improved image mining technique for brain tumor classification using pruned association rule with MARI algorithm is presented in this paper. The method proposed makes use of association rule mining technique to classify the CT scan brain images into three categories namely normal, benign and malign. It combines the low level features extracted from images and high level knowledge from specialists. The developed algorithm can assist the physicians for efficient classification with multiple keywords per image to improve the accuracy. The experimental result on prediagnosed database of brain i…
Ivan Franko Lviv National University（伊万·弗兰科利沃夫国立大学）
This paper describes the analysis of quantitative characteristics of frequent sets and association rules in the posts of Twitter microblogs, related to the discussion of "end of the world", which was allegedly predicted on December 21, 2012 due to the Mayan calendar. Discovered frequent sets and association rules characterize semantic relations between the concepts of analyzed subjects.The support for some fequent sets reaches the global maximum before the expected event with some time delay. Such frequent sets may be considered as predictive markers that characterize the significance of expec…
Predicting New Concept-Object Associations in Astronomy by Mining the Literature
Georgia Institute of Technology（佐治亚理工学院）；The Ohio State University（俄亥俄州立大学）；Harvard & Smithsonian（哈佛与史密森尼学会）；Oak Ridge National Laboratory（橡树岭国家实验室）；Argonne National Laboratory（阿贡国家实验室）
Privacy preserving association rule mining has triggered the development of many privacy preserving data mining techniques. A large fraction of them use randomized data distortion techniques to mask the data for preserving. This paper proposes a new transaction randomization method which is a combination of the fake transaction randomization method and a new per transaction randomization method. This method distorts the items within each transaction and ensures a higher level of data privacy in comparison to the previous approaches. The pertransaction randomization method involves a randomizat…
We present new methods for pruning and enhancing item- sets for text classification via association rule mining. Pruning methods are based on dependency syntax and enhancing methods are based on replacing words by their hyperonyms of various orders. We discuss the impact of these methods, compared to pruning based on tfidf rank of words.
