2011 3rd Conference on Data Mining and Optimization (DMO)最新文献_第2页

Applying Semantic Suffix Net to suffix tree clustering 语义后缀网在后缀树聚类中的应用

2011 3rd Conference on Data Mining and Optimization (DMO) Pub Date : 2011-06-28 DOI: 10.1109/DMO.2011.5976519

Jongkol Janruang, S. Guha

{"title":"Applying Semantic Suffix Net to suffix tree clustering","authors":"Jongkol Janruang, S. Guha","doi":"10.1109/DMO.2011.5976519","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976519","url":null,"abstract":"In this paper we consider the problem of clustering snippets returned from search engines. We propose a technique to invoke semantic similarity in the clustering process. Our technique improves on the well-known STC method, which is a highly efficient heuristic for clustering web search results. However, a weakness of STC is that it cannot cluster semantic similar documents. To solve this problem, we propose a new data structure to represent suffixes of a single string, called a Semantic Suffix Net (SSN). A generalized semantic suffix net is created to represent suffixes of a set of strings by using a new operator to partially combine nets. A key feature of this new operator is to find a joint point by using semantic similarity and string matching; net pairs combination then begins at that joint point. This logic causes the number of nodes and branches of a generalized semantic suffix net to decrease. The operator then uses the line of suffix links as a boundary to separate the net. A generalized semantic suffix net is then incorporated into the STC algorithm so that it can cluster semantically similar snippets. Experimental results show that the proposed algorithm improves upon conventional STC.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129446694","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}

引用次数: 11

MPCA-ARDA for solving course timetabling problems MPCA-ARDA用于解决课程排课问题

2011 3rd Conference on Data Mining and Optimization (DMO) Pub Date : 2011-06-28 DOI: 10.1109/DMO.2011.5976523

A. Abuhamdah, M. Ayob

{"title":"MPCA-ARDA for solving course timetabling problems","authors":"A. Abuhamdah, M. Ayob","doi":"10.1109/DMO.2011.5976523","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976523","url":null,"abstract":"This work presents a hybridization between Multi-Neighborhood Particle Collision Algorithm (MPCA) and Adaptive Randomized Descent Algorithm (ARDA) acceptance criterion to solve university course timetabling problems. The aim of this work is to produce an effective algorithm for assigning a set of courses, lecturers and students to a specific number of rooms and timeslots, subject to a set of constraints. The structure of the MPCA-ARDA resembles a Hybrid Particle Collision Algorithm (HPCA) structure. The basic difference is that MPCA-ARDA hybridize MPCA and ARDA acceptance criterion, whilst HPCA, hybridize MPCA and great deluge acceptance criterion. In other words, MPCA-ARDA employ adaptive acceptance criterion, whilst HPCA, employ deterministic acceptance criterion. Therefore, MPCA-ARDA has better capability of escaping from local optima compared to HPCA and MPCA. MPCA-ARDA attempts to enhance the trial solution by exploring different neighborhood structures to overcome the limitation in HPCA and MPCA. Results tested on Socha benchmark datasets show that, MPCA-ARDA is able to produce significantly good quality solutions within a reasonable time and outperformed some other approaches in some instances.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"83 5 Pt 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128659841","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}

引用次数: 4

Soft skills recommendation systems for IT jobs: A Bayesian network approach IT工作软技能推荐系统:贝叶斯网络方法

2011 3rd Conference on Data Mining and Optimization (DMO) Pub Date : 2011-06-28 DOI: 10.1109/DMO.2011.5976509

Azuraini Abu Bakar, Choo-Yee Ting

引用次数: 19

Intelligent Web caching using Adaptive Regression Trees, Splines, Random Forests and Tree Net 智能Web缓存使用自适应回归树，样条，随机森林和树网

2011 3rd Conference on Data Mining and Optimization (DMO) Pub Date : 2011-06-28 DOI: 10.1109/DMO.2011.5976513

Sarina Sulaiman, Siti Mariyam Hj. Shamsuddin, A. Abraham

{"title":"Intelligent Web caching using Adaptive Regression Trees, Splines, Random Forests and Tree Net","authors":"Sarina Sulaiman, Siti Mariyam Hj. Shamsuddin, A. Abraham","doi":"10.1109/DMO.2011.5976513","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976513","url":null,"abstract":"Web caching is a technology for improving network traffic on the internet. It is a temporary storage of Web objects (such as HTML documents) for later retrieval. There are three significant advantages to Web caching; reduced bandwidth consumption, reduced server load, and reduced latency. These rewards have made the Web less expensive with better performance. The aim of this research is to introduce advanced machine learning approaches for Web caching to decide either to cache or not to the cache server, which could be modelled as a classification problem. The challenges include identifying attributes ranking and significant improvements in the classification accuracy. Four methods are employed in this research; Classification and Regression Trees (CART), Multivariate Adaptive Regression Splines (MARS), Random Forest (RF) and TreeNet (TN) are used for classification on Web caching. The experimental results reveal that CART performed extremely well in classifying Web objects from the existing log data and an excellent attribute to consider for an accomplishment of Web cache performance enhancement.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133040425","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}

引用次数: 6

Optimisation model of selective cutting for Timber Harvest Planning in Peninsular Malaysia 马来西亚半岛木材采伐规划的选择性采伐优化模型

2011 3rd Conference on Data Mining and Optimization (DMO) Pub Date : 2011-06-28 DOI: 10.1109/DMO.2011.5976536

Munaisyah Abdullah, S. Abdullah, A. Hamdan, R. Ismail

引用次数: 0

A framework of rough reducts optimization based on PSO/ACO hybridized algorithms 基于粒子群算法和蚁群算法的粗糙约简优化框架

2011 3rd Conference on Data Mining and Optimization (DMO) Pub Date : 2011-06-28 DOI: 10.1109/DMO.2011.5976520

Lustiana Pratiwi, Y. Choo, A. Muda

引用次数: 6

A hybrid evaluation metric for optimizing classifier 一种用于分类器优化的混合评价指标

2011 3rd Conference on Data Mining and Optimization (DMO) Pub Date : 2011-06-28 DOI: 10.1109/DMO.2011.5976522

M. Hossin, M. Sulaiman, A. Mustapha, N. Mustapha, R. Rahmat

{"title":"A hybrid evaluation metric for optimizing classifier","authors":"M. Hossin, M. Sulaiman, A. Mustapha, N. Mustapha, R. Rahmat","doi":"10.1109/DMO.2011.5976522","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976522","url":null,"abstract":"The accuracy metric has been widely used for discriminating and selecting an optimal solution in constructing an optimized classifier. However, the use of accuracy metric leads the searching process to the sub-optimal solutions due to its limited capability of discriminating values. In this study, we propose a hybrid evaluation metric, which combines the accuracy metric with the precision and recall metrics. We call this new performance metric as Optimized Accuracy with Recall-Precision (OARP). This paper demonstrates that the OARP metric is more discriminating than the accuracy metric using two counter-examples. To verify this advantage, we conduct an empirical verification using a statistical discriminative analysis to prove that the OARP is statistically more discriminating than the accuracy metric. We also empirically demonstrate that a naive stochastic classification algorithm trained with the OARP metric is able to obtain better predictive results than the one trained with the conventional accuracy metric. The experiments have proved that the OARP metric is a better evaluator and optimizer in the constructing of optimized classifier.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131966476","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}

引用次数: 12

High order fuzzy time series for exchange rates forecasting 用于汇率预测的高阶模糊时间序列

2011 3rd Conference on Data Mining and Optimization (DMO) Pub Date : 2011-06-28 DOI: 10.1109/DMO.2011.5976496

L. Abdullah, I. Taib

引用次数: 7

Reducing network intrusion detection association rules using Chi-Squared pruning technique 利用Chi-Squared剪枝技术减少网络入侵检测关联规则

2011 3rd Conference on Data Mining and Optimization (DMO) Pub Date : 2011-06-28 DOI: 10.1109/DMO.2011.5976515

Ammar Fikrat Namik, Z. Othman

{"title":"Reducing network intrusion detection association rules using Chi-Squared pruning technique","authors":"Ammar Fikrat Namik, Z. Othman","doi":"10.1109/DMO.2011.5976515","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976515","url":null,"abstract":"Increasing number of computer networks now a day has increased the effort of putting networks in secure with various attack risk. Intrusion Detection System (IDS) is a popular tool to secure network. Applying data mining has increased the quality of intrusion detection neither as anomaly detection or misused detection from large scale network traffic transaction. Association rules is a popular technique to produce a quality misused detection. However, the weaknesses of association rules is the fact that it often produced with thousands rules which reduce the performance of IDS. This paper aims to show applying post-mining to reduce the number of rules and remaining the most quality rules to produce quality signature. The experiment conducted using two data set collected from KDD Cup 99. Each data set is partitioned into 4 data sets based on type of attacks (PROB, UR2, R2L and DOS). Each partition is mining using Apriori Algorithm, which later performing post-mining using Chi-Squared (χ2) computation techniques. The quality of rules is measured based on Chi-Square value, which calculated according the support, confidence and lift of each association rule. The experiment results shows applying post-mining has reduced the rules up to 98% and remaining the quality rules.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115359149","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}

引用次数: 3

Fuzzy projective clustering in high dimension data using decrement size of data 基于数据减量的高维数据模糊投影聚类

2011 3rd Conference on Data Mining and Optimization (DMO) Pub Date : 2011-06-28 DOI: 10.1109/DMO.2011.5976521

S. Mehdi Seyednejad, hamidreza musavi, S. Mohaddese Seyednejad, Tooraj Darabi

引用次数: 0