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A Novel Web Classification Algorithm Using Fuzzy Weighted Association Rules  [PDF]
Binu Thomas,G. Raju
ISRN Artificial Intelligence , 2013, DOI: 10.1155/2013/316913
Abstract: In associative classification method, the rules generated from association rule mining are converted into classification rules. The concept of association rule mining can be extended in web mining environment to find associations between web pages visited together by the internet users in their browsing sessions. The weighted fuzzy association rule mining techniques are capable of finding natural associations between items by considering the significance of their presence in a transaction. The significance of an item in a transaction is usually referred as the weight of an item in the transaction and finding associations between such weighted items is called fuzzy weighted association rule mining. In this paper, we are presenting a novel web classification algorithm using the principles of fuzzy association rule mining to classify the web pages into different web categories, depending on the manner in which they appear in user sessions. The results are finally represented in the form of classification rules and these rules are compared with the result generated using famous Boolean Apriori association rule mining algorithm. 1. Introduction Classification is a Data Mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data. For example, a classification model could be used to identify loan applicants in a bank as low, medium, or high credit risks. A classification task begins with a data set in which the class assignments are known. A classification model that predicts credit risk could be developed based on observed data for many loan applicants over a period of time. In addition to the historical credit rating, the data might track employment history, home ownership or rental, years of residence, number and type of investments, and so on. Credit rating would be the target, the other attributes would be the predictors, and the data for each customer would constitute a case. Classification techniques include decision trees, association rules, fuzzy systems, and neural networks. Classification has many applications in customer segmentation, business modeling, marketing, credit analysis, web mining and biomedical, and drug response modeling. Classification models include decision trees, Bayesian models, association rules, and neural nets. Although association rules have been predominantly used for data exploration and description, the interest in using them for prediction has rapidly increased in the Data Mining community. When
An Improved Apriori Algorithm for Association Rules  [PDF]
Mohammed Al-Maolegi,Bassam Arkok
Computer Science , 2014,
Abstract: There are several mining algorithms of association rules. One of the most popular algorithms is Apriori that is used to extract frequent itemsets from large database and getting the association rule for discovering the knowledge. Based on this algorithm, this paper indicates the limitation of the original Apriori algorithm of wasting time for scanning the whole database searching on the frequent itemsets, and presents an improvement on Apriori by reducing that wasted time depending on scanning only some transactions. The paper shows by experimental results with several groups of transactions, and with several values of minimum support that applied on the original Apriori and our implemented improved Apriori that our improved Apriori reduces the time consumed by 67.38% in comparison with the original Apriori, and makes the Apriori algorithm more efficient and less time consuming.
Mining Association Rules with Linguistic Cloud Models
用语言云模型发掘关联规则

LI De-yi,DI Kai-chang,LI De-ren,SHI Xue-mei,
李德毅
,邸凯昌,李德仁,史雪梅

软件学报 , 2000,
Abstract: This paper presents linguistic cloud models for knowledge representation and uncertainty handling in KDD.Multi-dimensional cloud models are introduced as the extension of one-dimensional ones.The digital characteristics of linguistic clouds well integrate the fuzziness and randomness of linguistic terms in a unified way.Conceptual hierarchies based on the models can bridge the gap between quantitative knowledge and qualitative knowledge.In order to discover strong association rules,attribute values are generalized at higher concept levels,allowing overlapping between neighbor attribute values or linguistic terms.And this kind of soft partitioning can mimic human being's thinking,while making the discovered knowledge robust.Combining the cloud model based generalization method with Apriori algorithm for mining association rules from a spatial database shows the benefits in effectiveness,efficiency and flexibility.
Prediction of protein-protein interaction types using association rule based classification
Sung Park, José A Reyes, David R Gilbert, Ji Kim, Sangsoo Kim
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-36
Abstract: This work addresses pattern discovery of the interaction sites for four different interaction types to characterize and uses them for the prediction of PPI types employing Association Rule Based Classification (ARBC) which includes association rule generation and posterior classification. We incorporated domain information from protein complexes in SCOP proteins and identified 354 domain-interaction sites. 14 interface properties were calculated from amino acid and secondary structure composition and then used to generate a set of association rules characterizing these domain-interaction sites employing the APRIORI algorithm. Our results regarding the classification of PPI types based on a set of discovered association rules shows that the discriminative ability of association rules can significantly impact on the prediction power of classification models. We also showed that the accuracy of the classification can be improved through the use of structural domain information and also the use of secondary structure content.The advantage of our approach is that we can extract biologically significant information from the interpretation of the discovered association rules in terms of understandability and interpretability of rules. A web application based on our method can be found at http://bioinfo.ssu.ac.kr/~shpark/picasso/ webciteProtein-Protein Interactions (PPIs) play a key role in many essential biological processes in cells, including signal transduction, transport, cellular motion and gene regulation. The comprehensive analysis of these biological interactions has been regarded as very significant for the understanding of underlying mechanisms involved in cellular processes.Computational approaches for the prediction of PPI based on atomic level interactions can accurately determine the binding affinity and the specificity of binding partners. Thus, structure based prediction methods including modeling of PPI by homology modeling, threading-based methods and pro
Association Rule Mining and Classifier Approach for 48-Hour Rainfall Prediction Over Cuddalore Station of East Coast of India  [cached]
S. Meganathan,T.R. Sivaramakrishnan
Research Journal of Applied Sciences, Engineering and Technology , 2013,
Abstract: The methodology of data mining techniques has been presented for the rain forecasting models for the Cuddalore (11°43′ N/79°49′ E) station of Tamilnadu in East Coast of India. Data mining approaches like classification and association mining was applied to generate results for rain prediction before 48 hour of the actual occurrence of the rain. The objective of this study is to demonstrate what relationship models are there between various atmospheric variables and to interconnect these variables according to the pattern obtained out of data mining technique. Using this approach rainfall estimates can be obtained to support the decisions to launch cloud-seeding operations. There are 3 main parts in this study. First, the obtained raw data was filtered using discretization approach based on the best fit ranges. Then, association mining has been performed on it using Predictive Apriori algorithm. Thirdly, the data has been validated using K* classifier approach. Results show that the overall classification accuracy of the data mining technique is satisfactory
Optimization of Association Rule Mining Apriori Algorithm Using ACO  [PDF]
Badri Patel,Vijay K Chaudhari,Rajneesh K Karan,YK Rana
International Journal of Soft Computing & Engineering , 2011,
Abstract: Association rule mining is an important topic in data mining field. In a given large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. Apriori algorithm that generates all significant association rules between items in the database. On the basis of the association rule mining and Apriori algorithm, this paper proposes an improved algorithm based on the Ant Colony Optimization algorithm. We can optimize the result generated by Apriori algorithm using Ant colony optimization algorithm. The algorithm improved result produces by Apriori algorithm. Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies. ACO was introduced by Dorigo and has evolved significantly in the last few years.
Mining Video Association Rules Based on Weighted Temporal Concepts  [PDF]
V.Vijayakumar,R.Nedunchezhian
International Journal of Computer Science Issues , 2012,
Abstract: Discovery of video association rules has been found useful in many applications to explore the video knowledge such as video indexing, summarization, classification and semantic event detection. The traditional classical association rule mining algorithms can not apply directly to the video database. It differs in two ways such as spatial and temporal properties of the video database and significance of the items in the vide cluster sequence. The proposed paper discovers significant relationships in video sequence using weighted temporal concepts. The weights of the video items take the quality of transactions into considerations using modified link-based models. The proposed Modified HITS based weighted temporal concept did not require pre-assigned weights. The mined association rules have more practical significance. This strategy identifies the valuable rules comparing with Apriori based video sequence algorithm. We also present results of applying these algorithms to a synthetic data set, which show the effectiveness of our algorithm.
Association Rule Generation Using Apriori Mend Algorithm for Student s Placement  [PDF]
Magdalene Delighta Angeline,Samuel Peter James
International Journal of Emerging Sciences , 2012,
Abstract: The association rules are used to find interesting rules from large collections of data which expresses an association between items or sets of items. The usefulness of this technique is to address typical data mining problems is best. In order to show the effective relation of data, student placement was chosen and experiments were carried out which shows the best rules with 92.86% confidence while comparing with the previous Apriori approach. In this paper Apriori Mend algorithm was discussed which provide better result in mining association rules for Student’s placement in industry.
Finding the Chances and Prediction of Cancer through Apriori Algorithm with Transaction Reduction
Shashank Singh,Manoj Yadav,Hitesh Gupta
International Journal of Advanced Computer Research , 2012,
Abstract: Frequent pattern mining is an important task ofdata mining. It is essential for mining association,relevant and interesting links. In addition, it iswidely used in data classification, clustering andother data mining tasks. Many effective, scalablealgorithms have been developed in terms of frequentpattern mining. The Apriori algorithm is a classicalfrequent item sets generation algorithm and amilestone in the development of data mining. In thispaper we apply the apriori algorithm withtransaction reduction on cancer symptoms. Weconsider five different types of cancer andaccording to the classification we generate thecandidate sets and minimum support to find thespreading of cancer. By this we can find thesymptoms by which the cancer is spreading moreand also about the highest spreading cancer type.
An Algorithm for Mining of Association Rules for the Information Communication Network Alarms Based on Swarm Intelligence  [PDF]
Yang Wang,Guocai Li,Yakun Xu,Jie Hu
Mathematical Problems in Engineering , 2014, DOI: 10.1155/2014/894205
Abstract: Due to the centralized management of information communication network, the network operator have to face these pressures, which come from the increasing network alarms and maintenance efficiency. The effective analysis on mining of the network alarm association rules is achieved by incorporating classic data association mining algorithm and swarm intelligence optimization algorithm. From the related concept of the information communication network, the paper analyzes the data characteristics and association logic of the network alarms. Besides, the alarm data are preprocessed and the main standardization information fields are screened. The APPSO algorithm is proposed on the basis of combining the evaluation method for support and confidence coefficient in the Apriori (AP) algorithm as well as the particle swarm optimization (PSO) algorithm. By establishing a sparse linked list, the algorithm is able to calculate the particle support thus further improving the performance of the APPSO algorithm. Based on the test for the network alarm data, it is discovered that rational setting of the particle swarm scale and number of iterations of the APPSO algorithm can be used to mine the vast majority and even all of the association rules and the mining efficiency is significantly improved, compared with Apriori algorithm. 1. Introduction The operation and maintenance management of information communication network mainly refers to timely discovery, locating and handling of any network fault to ensure smooth and efficient operation as well as guarantee in major emergencies pertinent to network operation, complaints about network quality from customers, assessment and analysis of network quality, prediction of planning, construction, and so forth. The time consumed during fault location and judgment in the application layer of a large-scale network accounts for 93% of its total time for failure of recovery [1]. The huge network structure and multifunctional device types also bring about large amounts of alarm data due to such characteristics of the information communication network as topological structure densification, network device microminiaturization, communication board precision, and so forth. Therefore, the foundation of the network operation and maintenance is the effective management of the network alarms. As an important supporting means for network operation and maintenance management, network management system directly influences the quality of service which the information communication network provides to its customers [2]. The network management
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