Template-Type: ReDIF-Article 1.0
Author-Name: Massimiliano Ferrara
Author-X-Name-First: Massimiliano
Author-X-Name-Last: Ferrara
Author-Name: Diego Fosso
Author-X-Name-First: Diego
Author-X-Name-Last: Fosso
Author-Name: Davide Lanatà
Author-X-Name-First: Davide
Author-X-Name-Last: Lanatà
Author-Name: Roberto Mavilia
Author-X-Name-First: Roberto
Author-X-Name-Last: Mavilia
Author-Name: Domenico Ursino
Author-X-Name-First: Domenico
Author-X-Name-Last: Ursino
Title: A social network analysis based approach to extracting knowledge patterns about innovation geography from patent databases
Abstract:
Patents have been one of the main topics investigated in several fields of scientific literature. Currently, data about patents is rapidly increasing, and the adoption of data mining and big-data-centred approaches to investigating them appears compulsory. Among these last approaches, social network analysis (SNA) is extremely promising. In this paper, we propose an SNA-based approach to extracting knowledge patterns about patent inventors and their collaborations. Our approach is extremely general and can be exploited to investigate patents of any country. It allows the analysis of some issues that have not been considered in the past, such as the presence of 'power inventors' in a country, the existence of a backbone and of possible cliques among them, the influence and the benefits of power inventors on their co-inventors and, more in general, in the R%D activities of their country. All these issues represent innovation geography knowledge patterns that can be extracted, thanks to our approach.
Journal: Int. J. of Data Mining, Modelling and Management
Pages: 23-72
Issue: 1
Volume: 10
Year: 2018
Keywords: patents; knowledge pattern extraction; social network analysis; SNA; power inventors; innovation geography.
File-URL: http://www.inderscience.com/link.php?id=89627
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Template-Type: ReDIF-Article 1.0
Author-Name: Mohamed Ramzi Haddad
Author-X-Name-First: Mohamed Ramzi
Author-X-Name-Last: Haddad
Author-Name: Hajer Baazaoui
Author-X-Name-First: Hajer
Author-X-Name-Last: Baazaoui
Title: An adaptive and interactive recommendation model for consumers' behaviours prediction
Abstract:
Recommendation algorithms aim at predicting customers' interests and purchases using different ideas and hypotheses. Consequently, system designers need to choose the recommendation approach that is the most suitable with regard to their products' nature and consumers' behaviours within the application field. In this paper, we propose an adaptive recommendation model based on statistical modelling to assist consumers facing choice overload by predicting their interests and consumption behaviours. We also propose a dynamic variant of the model taking into account the recommendations' time-value during interactive online recommendation scenarios. Our proposal has endured a two-fold evaluation. First, we conducted an offline comparative study on the MovieLens recommendation dataset in order to assess our model's performance with regard to several widely adopted recommendation techniques. Then, the model was evaluated within a real time online news recommendation platform to highlight its adaptability, scalability and efficiency in a highly interactive application domain.
Journal: Int. J. of Data Mining, Modelling and Management
Pages: 89-111
Issue: 1
Volume: 10
Year: 2018
Keywords: adaptive recommendation model; interactive recommendation; continuous recommendation; consumer behaviour modelling and prediction.
File-URL: http://www.inderscience.com/link.php?id=89628
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Handle: RePEc:ids:ijdmmm:v:10:y:2018:i:1:p:89-111

Template-Type: ReDIF-Article 1.0
Author-Name: Amina Kemmar
Author-X-Name-First: Amina
Author-X-Name-Last: Kemmar
Author-Name: Yahia Lebbah
Author-X-Name-First: Yahia
Author-X-Name-Last: Lebbah
Author-Name: Samir Loudni
Author-X-Name-First: Samir
Author-X-Name-Last: Loudni
Title: Interval graph mining
Abstract:
Frequent subgraph mining is a difficult data mining problem aiming to find the exact set of frequent subgraphs into a database of graphs. Current subgraph mining approaches make use of the canonical encoding which is one of the key operations. It is well known that canonical encodings have an exponential time complexity. Consequently, mining all frequent patterns for large and dense graphs is computationally expensive. In this paper, we propose an interval approach to handle canonicity, leading to two encodings, <i>lower and upper encodings</i>, with a polynomial time complexity, allowing to tightly enclose the exact set of frequent subgraphs. These two encodings lead to an interval graph mining algorithm where two minings are launched in parallel, a lower mining (resp. upper mining) using the lower (resp. upper) encoding. The interval graph mining approach has been implemented within the state of the art Gaston miner. Experiments performed on synthetic and real graph databases coming from stock market and biological datasets show that our interval graph mining is effective on dense graphs.
Journal: Int. J. of Data Mining, Modelling and Management
Pages: 1-22
Issue: 1
Volume: 10
Year: 2018
Keywords: graphmining; interval approach; frequent subgraph discovery; graph encoding; subgraph isomorphism; graph isomorphism.
File-URL: http://www.inderscience.com/link.php?id=89629
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Template-Type: ReDIF-Article 1.0
Author-Name: Rasha A. Mohammed
Author-X-Name-First: Rasha A.
Author-X-Name-Last: Mohammed
Author-Name: Mehdi G. Duaimi
Author-X-Name-First: Mehdi G.
Author-X-Name-Last: Duaimi
Title: Association rules mining using cuckoo search algorithm
Abstract:
Association rules mining (ARM) is a fundamental and widely used data mining technique to achieve useful information about data. The traditional ARM algorithms are degrading computation efficiency by mining too many association rules which are not appropriate for a given user. Recent research in (ARM) is investigating the use of metaheuristic algorithms which are looking for only a subset of high-quality rules. In this paper, a modified discrete cuckoo search algorithm for association rules mining DCS-ARM is proposed for this purpose. The effectiveness of our algorithm is tested against a set of well-known transactional databases. Results indicate that the proposed algorithm outperforms the existing metaheuristic methods.
Journal: Int. J. of Data Mining, Modelling and Management
Pages: 73-88
Issue: 1
Volume: 10
Year: 2018
Keywords: data mining; ARM; association rules mining; DCS; discrete cuckoo search; metaheuristic algorithm.
File-URL: http://www.inderscience.com/link.php?id=89630
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Template-Type: ReDIF-Article 1.0
Author-Name: Anirban Chakrabarty
Author-X-Name-First: Anirban
Author-X-Name-Last: Chakrabarty
Author-Name: Sudipta Roy
Author-X-Name-First: Sudipta
Author-X-Name-Last: Roy
Title: An efficient context-aware agglomerative fuzzy clustering framework for plagiarism detection
Abstract:
Plagiarism refers to the act of copying content without acknowledging the original source. Though there are several existing commercial tools for plagiarism detection, still plagiarism is tricky and challenging due to the rise in volume of online publications. Existing plagiarism detection methods use paraphrasing, sentence and key-word matching, but such techniques has not been very effective. In this work, a framework for fuzzy based plagiarism detection is proposed using a context-aware agglomerative clustering approach with an improved time complexity. The work aims in retrieving key concepts at word, sentence and paragraph level by integrating semantic features in a novel optimisation function to detect plagiarism effectively. The notion of fuzzy clustering has been applied to improve the robustness and consistency of results for clustering multi-disciplinary papers. The experimental analysis is supported by comparison with other contemporary techniques which indicate the superiority of proposed approach for plagiarism detection.
Journal: Int. J. of Data Mining, Modelling and Management
Pages: 188-208
Issue: 2
Volume: 10
Year: 2018
Keywords: fuzzy clustering; context similarity; plagiarism detection; spanning tree; agglomerative clustering; validity index; constrained objective function.
File-URL: http://www.inderscience.com/link.php?id=92533
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Handle: RePEc:ids:ijdmmm:v:10:y:2018:i:2:p:188-208

Template-Type: ReDIF-Article 1.0
Author-Name: Farek Lazhar
Author-X-Name-First: Farek
Author-X-Name-Last: Lazhar
Title: Mining hidden opinions from objective sentences
Abstract:
Sentiment analysis and opinion mining is a very popular and active research area in natural language processing, it deals with structured and unstructured data to identify and extract people's opinions, sentiments and emotions in many resources of subjectivity such as product reviews, blogs, social networks, etc. All existing feature-level opinion mining approaches deal with the detection of subjective sentences and eliminate objective ones before extracting explicit features and their related positive or negative polarities. However, objective sentences can carry implicit opinions and a lack attention given to such sentences can adversely affect the obtained results. In this paper, we propose a classification-based approach to extract implicit opinions from objective sentences. Firstly, we apply a rule-based approach to extract explicit feature-opinion pairs from subjective sentences. Secondly, in order to build a classification model, we construct a training corpus based on extracted explicit feature-opinion pairs and subjective sentences. Lastly, mining implicit feature-opinion pairs from objective sentences is formulated into a text classification problem using the model previously built. Tested on customer reviews in three different domains, experimental results show the effectiveness of mining opinions from objective sentences.
Journal: Int. J. of Data Mining, Modelling and Management
Pages: 113-126
Issue: 2
Volume: 10
Year: 2018
Keywords: opinion mining; hidden opinion; objectivity; subjectivity; supervised learning.
File-URL: http://www.inderscience.com/link.php?id=92534
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Handle: RePEc:ids:ijdmmm:v:10:y:2018:i:2:p:113-126

Template-Type: ReDIF-Article 1.0
Author-Name: Poonam Goyal
Author-X-Name-First: Poonam
Author-X-Name-Last: Goyal
Author-Name: N. Mehala
Author-X-Name-First: N.
Author-X-Name-Last: Mehala
Author-Name: Divyansh Bhatia
Author-X-Name-First: Divyansh
Author-X-Name-Last: Bhatia
Author-Name: Navneet Goyal
Author-X-Name-First: Navneet
Author-X-Name-Last: Goyal
Title: Topical document clustering: two-stage post processing technique
Abstract:
Clustering documents is an essential step in improving efficiency and effectiveness of information retrieval systems. We propose a two-phase split-merge (SM) algorithm, which can be applied to topical clusters obtained from existing query-context-aware document clustering algorithms, to produce soft topical document clusters. The SM is a post-processing technique which combines the advantages of document and feature-pivot topical document clustering approaches. The split phase splits the topical clusters by relating them to the topics obtained by disambiguating web search results, and converts them into homogeneous soft clusters. In the merge phase, similar clusters are merged by feature-pivot approach. The SM is tested on the outcome of two hierarchical query-context aware document clustering algorithms on different datasets including TREC session-track 2011 dataset. The obtained topical-clusters are also updated by an incremental approach with the progress in the data stream. The proposed algorithm improves the quality of clustering appreciably in all the experiments conducted.
Journal: Int. J. of Data Mining, Modelling and Management
Pages: 127-170
Issue: 2
Volume: 10
Year: 2018
Keywords: topical clustering; query clustering; query context; document clustering; incremental clustering; soft clustering.
File-URL: http://www.inderscience.com/link.php?id=92536
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Handle: RePEc:ids:ijdmmm:v:10:y:2018:i:2:p:127-170

Template-Type: ReDIF-Article 1.0
Author-Name: Sihem Khemakhem
Author-X-Name-First: Sihem
Author-X-Name-Last: Khemakhem
Author-Name: Younes Boujelbene
Author-X-Name-First: Younes
Author-X-Name-Last: Boujelbene
Title: Support vector machines for credit risk assessment with imbalanced datasets
Abstract:
Support vector machines (SVM) have a limited performance in credit scoring issues due to the imbalanced data sets in which the number of unpaid is lower than paid loans. In this work, we developed an SVM model with more kernels on a set of imbalanced data and suggested two data resampling alternatives: random over sampling (ROS) and synthetic minority oversampling technique (SMOTE). The aim of this work is to explore the relevance of re-sampling data with the SVM technique for an accurate credit risk prediction rate to the class imbalance constraint. The performance criteria chosen to evaluate the suggested technique were accuracy, sensitivity specificity, error type I, error type II, G-mean and the area under the receiver operating characteristic curve (AUC). Significant empirical results obtained from an experimental study of a real imbalanced database of loans granted by a Tunisian bank demonstrated the performance improvement thanks to sampling strategies in SVM, thus leading to a better prediction accuracy of the creditworthiness of borrowers.
Journal: Int. J. of Data Mining, Modelling and Management
Pages: 171-187
Issue: 2
Volume: 10
Year: 2018
Keywords: credit scoring; support vector machines; SVM; synthetic minority oversampling technique; SMOTE; random over sampling; ROS; credit risk assessment; imbalanced datasets; performance criteria; Tunisian bank; creditworthiness prediction accuracy.
File-URL: http://www.inderscience.com/link.php?id=92538
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Handle: RePEc:ids:ijdmmm:v:10:y:2018:i:2:p:171-187

Template-Type: ReDIF-Article 1.0
Author-Name: Abdurrahman A. Nasr
Author-X-Name-First: Abdurrahman A.
Author-X-Name-Last: Nasr
Title: ABCD: agent based model for document classification
Abstract:
Document classification is the task of analysing, identifying and categorising collection of documents into their annotated classes based on their contents. This paper presents ABCD as an agent-based classifier for documents. ABCD is autonomous by depending on software agents in collecting and distributing documents, and smart by exploiting machine learning techniques to train the underlying classifier. As such, the system consists of two essential components, namely, the agent component and the classification component. To be comprehensive and to facilitate comparative results, five statistical classifiers are exploited. These classifiers are based on Naïve Bayes (NB), Hidden Markov Model (HMM), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), Support Vector Machine (SVM) and Random Forest (RF) algorithms. The proposed model is experimentally tested on both BBC news articles dataset and News Aggregator dataset from artificial intelligence lab. The obtained results indicate the superiority of the Random Forest algorithm for classifying unimodal documents.
Journal: Int. J. of Data Mining, Modelling and Management
Pages: 250-268
Issue: 3
Volume: 10
Year: 2018
Keywords: software agent; supervised learning; random forest; document classification; unimodal document; multi-agent system; data mining.
File-URL: http://www.inderscience.com/link.php?id=93878
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Handle: RePEc:ids:ijdmmm:v:10:y:2018:i:3:p:250-268

Template-Type: ReDIF-Article 1.0
Author-Name: Shashi Mehrotra
Author-X-Name-First: Shashi
Author-X-Name-Last: Mehrotra
Author-Name: Shruti Kohli
Author-X-Name-First: Shruti
Author-X-Name-Last: Kohli
Author-Name: Aditi Sharan
Author-X-Name-First: Aditi
Author-X-Name-Last: Sharan
Title: To identify the usage of clustering techniques for improving search result of a website
Abstract:
Clustering has drawn much attention to research community due to its advantages and wide applications. However, clustering is a challenging problem, as many factors play a significant role. The same algorithm may generate different output if there is a change in parameters, presentation order or similarity measure. The search option is used excessively on almost every website. Grouping the search results in various folders will improve web browsing and that can be achieved by applying clustering over results. Clustering web elements facilitate data analysis in various ways. In this paper, we present well-known clustering algorithms and identify their different usages for the web elements. The paper discusses some significant work conducted in this field.
Journal: Int. J. of Data Mining, Modelling and Management
Pages: 229-249
Issue: 3
Volume: 10
Year: 2018
Keywords: clustering algorithm; distance measure; web analytics; complexity.
File-URL: http://www.inderscience.com/link.php?id=93879
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Handle: RePEc:ids:ijdmmm:v:10:y:2018:i:3:p:229-249

Template-Type: ReDIF-Article 1.0
Author-Name: Konstantinos Kontos
Author-X-Name-First: Konstantinos
Author-X-Name-Last: Kontos
Author-Name: Manolis Maragoudakis
Author-X-Name-First: Manolis
Author-X-Name-Last: Maragoudakis
Title: Machine learning for water bodies identification from satellite images
Abstract:
Examining satellite images on residential areas and more particularly bodies of water such as swimming pools are of great interest in the field of image mining. Initially, the unobstructed water consumption for pool operation can lead to the reduction of water supplies especially during summer months, a fact that can influence water sources for firefighting. Moreover, they may serve as potential mosquito habitat, especially if they are surrounded by dense vegetation. Towards this direction, this paper presents an efficient classification system for identifying swimming pools from satellite images. A new method of trainable segmentation is presented for feature extraction. In this study, a support vector machine algorithm is used for reducing the feature set to the more appropriate one. The proposed method was tested on different areas of Greece with an overall accuracy of 99.82% that was achieved by using an ensemble algorithm.
Journal: Int. J. of Data Mining, Modelling and Management
Pages: 209-228
Issue: 3
Volume: 10
Year: 2018
Keywords: satellite images; feature extraction; image processing; pool detection; trainable segmentation; data mining; SVM algorithms; decision trees; image classification; image mining; AdaBoost.
File-URL: http://www.inderscience.com/link.php?id=93881
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Handle: RePEc:ids:ijdmmm:v:10:y:2018:i:3:p:209-228

Template-Type: ReDIF-Article 1.0
Author-Name: Bikram Keshari Mishra
Author-X-Name-First: Bikram Keshari
Author-X-Name-Last: Mishra
Author-Name: Amiya Kumar Rath
Author-X-Name-First: Amiya Kumar
Author-X-Name-Last: Rath
Title: Improving the efficacy of clustering by using far enhanced clustering algorithm
Abstract:
There are several aspects on which research works are carried out on clustering. The prime focus is on finding the near optimal cluster centres and determining the best possible clusters. Hence, we have emphasised our work on finding a technique which contemplates on these facets in a way which is far more efficient than several novel approaches. In this paper, we have examined four varieties of clustering algorithms namely; K-Means, FEKM, ECM and proposed FECA implemented on varying data sets. We used few internal cluster validity indices like Dunn's index, Davies-Bouldin's index, Silhouette Coefficient, C index and Calinski index for quantitative evaluation of the clustering results obtained. The results obtained from simulation were compared, and as per our expectation it was found that, the quality of clustering produced by FECA is far more satisfactory than the others. Almost every value of validity indices used give encouraging results for FECA, implying good cluster formation. Further experiments support that the proposed algorithm also produces minimum quantisation error almost for all the data sets used.
Journal: Int. J. of Data Mining, Modelling and Management
Pages: 269-292
Issue: 3
Volume: 10
Year: 2018
Keywords: cluster analysis; cluster validation; optimal centroid; K-means; far efficient K-means; FEKM; far enhanced clustering algorithm; FECA; enhanced clustering methodology; ECM.
File-URL: http://www.inderscience.com/link.php?id=93886
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Handle: RePEc:ids:ijdmmm:v:10:y:2018:i:3:p:269-292

Template-Type: ReDIF-Article 1.0
Author-Name: Doosung Hwang
Author-X-Name-First: Doosung
Author-X-Name-Last: Hwang
Author-Name: Youngju Son
Author-X-Name-First: Youngju
Author-X-Name-Last: Son
Title: Prototype-based classification and error analysis under bootstrapping strategy
Abstract:
A prototype-based classification is proposed to select handfuls of class data for learning rules and prediction. A class point is considered as a prototype if it forms a hypersphere that represents a part of class area measured by any distance metric and class label. The prototype selection algorithm, formulated by a set covering optimisation, selects the number of within-class points that is as small as possible, while preserving class covering regions for the unknown data distribution. The upper bound of the error is analysed to compare the effectiveness of the prototype-based classification with the Bayes classifier. Under a bootstrapping strategy and the 0/1 loss, the bias and variance components are driven from a generalisation error without assuming the unknown distribution of a given problem. This analysis provides a way to evaluate prototype-based models and select the optimal model estimate for any standard classifier. The experiments show that the proposed approach is very competitive when compared to the nearest neighbour and the Bayes classifier and efficient in choosing prototypes in terms of class covering regions, data size and computation time.
Journal: Int. J. of Data Mining, Modelling and Management
Pages: 293-313
Issue: 4
Volume: 10
Year: 2018
Keywords: class prototype; set covering optimisation; greedy method; nearest neighbour; error analysis.
File-URL: http://www.inderscience.com/link.php?id=95352
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Handle: RePEc:ids:ijdmmm:v:10:y:2018:i:4:p:293-313

Template-Type: ReDIF-Article 1.0
Author-Name: Phimphaka Taninpong
Author-X-Name-First: Phimphaka
Author-X-Name-Last: Taninpong
Author-Name: Sudsanguan Ngamsuriyaroj
Author-X-Name-First: Sudsanguan
Author-X-Name-Last: Ngamsuriyaroj
Title: Tree-based text stream clustering with application to spam mail classification
Abstract:
This paper proposes a new text clustering algorithm based on a tree structure. The main idea of the clustering algorithm is a sub-tree at a specific node represents a document cluster. Our clustering algorithm is a single pass scanning algorithm which traverses down the tree to search for all clusters without having to predefine the number of clusters. Thus, it fits our objectives to produce document clusters having high cohesion, and to keep the minimum number of clusters. Moreover, an incremental learning process will perform after a new document is inserted into the tree, and the clusters will be rebuilt to accommodate the new information. In addition, we applied the proposed clustering algorithm to spam mail classification and the experimental results show that tree-based text clustering spam filter gives higher accuracy and specificity than the cobweb clustering, naïve Bayes and KNN.
Journal: Int. J. of Data Mining, Modelling and Management
Pages: 353-370
Issue: 4
Volume: 10
Year: 2018
Keywords: clustering; data mining; text clustering; text mining; text stream; tree-based clustering; spam; spam classification; text classification.
File-URL: http://www.inderscience.com/link.php?id=95354
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Handle: RePEc:ids:ijdmmm:v:10:y:2018:i:4:p:353-370

Template-Type: ReDIF-Article 1.0
Author-Name: Sepideh Mohammadkhani
Author-X-Name-First: Sepideh
Author-X-Name-Last: Mohammadkhani
Author-Name: Mansour Esmaeilpour
Author-X-Name-First: Mansour
Author-X-Name-Last: Esmaeilpour
Title: A new method for behavioural-based malware detection using reinforcement learning
Abstract:
Malware is - the abbreviation for malicious software - a comprehensive term for software that is deliberately created to perform an unauthorised and often harmful function. Viruses, backdoors, key-loggers, Trojans, password thieves' software, spyware, adwares are number of malware samples. Previously, calling something a virus or Trojan was enough. However, methods of contamination are developed, the term virus and other malware definition was not satisfactory for all types of malicious programs. This research focus on clustering the malware according to the malware features. To avoid the dangers of malware, some applications have been created to track them down. This paper presents a new method for detection of malware using reinforcement learning. The result demonstrates that the proposed method can detect the malware more accurate.
Journal: Int. J. of Data Mining, Modelling and Management
Pages: 314-330
Issue: 4
Volume: 10
Year: 2018
Keywords: antivirus; AVS; malware; reinforcement learning.
File-URL: http://www.inderscience.com/link.php?id=95372
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Handle: RePEc:ids:ijdmmm:v:10:y:2018:i:4:p:314-330

Template-Type: ReDIF-Article 1.0
Author-Name: K. Geetha
Author-X-Name-First: K.
Author-X-Name-Last: Geetha
Author-Name: A. Kannan
Author-X-Name-First: A.
Author-X-Name-Last: Kannan
Title: Efficient spatial query processing for KNN queries using well organised net-grid partition indexing approach
Abstract:
In recent years, most of the applications use mobile devices with geographical positioning systems support for providing location-based services. However, the queries sent through the mobile devices to obtain such services consume more time for processing due to the size of the spatial data. In order to solve this problem, an efficient indexing method for providing effective query processing services in mobile computing environments is proposed. This indexing method increases the efficiency of the query retrieval in mobile network environments. Since, all the existing mobile-based network applications utilise the node to node access of spatial objects for processing the query, the mobile query retrieval part in spatial databases is becoming the greatest disadvantage by consuming more time to process the query. The experimental results carried out using the proposed net-grid-based partition index approach show that the proposed model provides fast retrieval with high accuracy in processing of spatial queries.
Journal: Int. J. of Data Mining, Modelling and Management
Pages: 331-352
Issue: 4
Volume: 10
Year: 2018
Keywords: cache mechanism; KNN queries; location-based services; LBS; mobile environments; partition index; query processing; spatial data management; spatial networks; spatial query; wireless data broadcast.
File-URL: http://www.inderscience.com/link.php?id=95378
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Handle: RePEc:ids:ijdmmm:v:10:y:2018:i:4:p:331-352