Sequential Patterns Postprocessing for Structural Relation Patterns Mining

2011 ◽  
pp. 216-234
Author(s):  
Lu Jing ◽  
Chen Weiru ◽  
Adjei Osei ◽  
Keech Malcolm
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Efficient Method ◽  
Sequential Patterns ◽  
Data Mining Technique ◽  
Structural Patterns ◽  
Structural Relation ◽  
Integrative Framework ◽  
Complex Structural ◽  
Over Time

Sequential patterns mining is an important data-mining technique used to identify frequently observed sequential occurrence of items across ordered transactions over time. It has been extensively studied in the literature, and there exists a diversity of algorithms. However, more complex structural patterns are often hidden behind sequences. This article begins with the introduction of a model for the representation of sequential patterns—Sequential Patterns Graph—which motivates the search for new structural relation patterns. An integrative framework for the discovery of these patterns–Postsequential Patterns Mining–is then described which underpins the postprocessing of sequential patterns. A corresponding data-mining method based on sequential patterns postprocessing is proposed and shown to be effective in the search for concurrent patterns. From experiments conducted on three component algorithms, it is demonstrated that sequential patterns-based concurrent patterns mining provides an efficient method for structural knowledge discovery.

Sequential Patterns Postprocessing for Structural Relation Patterns Mining

2010 ◽  
pp. 787-806
Author(s):  
Jing Lu ◽  
Weiru Chen ◽  
Osei Adjei ◽  
Malcolm Keech
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Efficient Method ◽  
Sequential Patterns ◽  
Data Mining Technique ◽  
Structural Patterns ◽  
Structural Relation ◽  
Integrative Framework ◽  
Complex Structural ◽  
Over Time

Sequential patterns mining is an important data-mining technique used to identify frequently observed sequential occurrence of items across ordered transactions over time. It has been extensively studied in the literature, and there exists a diversity of algorithms. However, more complex structural patterns are often hidden behind sequences. This article begins with the introduction of a model for the representation of sequential patterns—Sequential Patterns Graph—which motivates the search for new structural relation patterns. An integrative framework for the discovery of these patterns–Postsequential Patterns Mining–is then described which underpins the postprocessing of sequential patterns. A corresponding data-mining method based on sequential patterns postprocessing is proposed and shown to be effective in the search for concurrent patterns. From experiments conducted on three component algorithms, it is demonstrated that sequential patterns-based concurrent patterns mining provides an efficient method for structural knowledge discovery.

scholarly journals Augmented intelligence in educational data mining

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Tapani Toivonen ◽  
Ilkka Jormanainen ◽  
Markku Tukiainen
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Predictive Model ◽  
Iterative Process ◽  
Educational Data Mining ◽  
Parameter Tuning ◽  
End Users ◽  
Tree Models ◽  
Level Of Knowledge ◽  
Over Time

Abstract Educational data mining (EDM) processes have shifted towards open-ended processes with visualizations and parameter and predictive model adjusting. Data and models in hyperdimensions can be visualized for end-users with popular data mining platforms such as Weka and RapidMiner. Multiple studies have shown how the adjusting and even creating the decision tree classifiers help EDM end-users to better comprehend the dataset and the context where the data has been collected. To harness the power of such open-ended approach in EDM, we introduce a novel Augmented Intelligence method and a cluster analysis algorithm Neural N-Tree. These contributions allow EDM end-users to analyze educational data in an iterative process where the knowledge discovery and the accuracy of the predictive model generated by the algorithm increases over time through the interactions between the models and the end-users. In contrast to other similar approaches, the key in our method is in the model adjusting and not in parameter tuning. We report a study where the potential EDM end-users clustered data from an education setting and interacted with Neural N-Tree models by following Augmented Intelligence method. The findings of the study suggest that the accuracy of the models evolve over time and especially the end-users who have a adequate level of knowledge from data mining benefit from the method. Moreover, the study indicates that the knowledge discovery is possible through AUI.

scholarly journals New Patterns And Techniques In Knowledge Discovery Through Data Mining

2013 ◽  
Vol 7 (1) ◽  
pp. 947-954
Author(s):  
Tiruveedula Gopi Krishna ◽  
Dr.Mohamed Abdeldaiem Abdelhadi ◽  
M.Madhusudhana Subramanian
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Knowledge Discovery ◽  
Large Datasets ◽  
Automatic Process ◽  
Surface Complex ◽  
Structural Patterns ◽  
Very Large Datasets ◽  
Complex Relationships ◽  
Decision Support Process

The main focus of this paper discussion was on mining and its set of techniques used in an automated approach to exhaustively explore and bring to the surface complex relationships in very large datasets. We discussed how a decision support process can be used to search for patterns of information in data. And also discussed different techniques for finding and describing structural patterns in data as well. Knowledge Discovery is a concept that describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data. We discussed all automatic and semi-automatic process of discovering the patterns in data that how it leads to some advantage in businesses to make knowledge driven decisions, which help the company to succeed and compete.

Quality and Effectiveness of ERP Software

2020 ◽  
pp. 28-52
Author(s):  
Stephen Makau Mutua ◽  
Raphael Angulu
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Knowledge Discovery ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Data Repository ◽  
Quality Information ◽  
Erp System ◽  
Entry Points ◽  
Over Time

Over time, the adoption of ERP systems has been wide across many small, medium, and large organizations. An ERP system is supposed to inform the strategic decision making of the organization; therefore, the information drawn from the ERP system is as important as the data stored in it. Poor data quality affects the quality information in it. Data mining is used to discover trends and patterns of an organization. This chapter looks into the way of integrating these data mining into an ERP system. This is conceptualized in three crucial views namely the outer, inner, and the knowledge discovery view. The outer view comprises of the collection of various entry points, the inner view contains the data repository, and the knowledge discovery view offers the data mining component. Since the focus is data mining, the two strategies of supervised and unsupervised are discussed. The chapter then concludes by presenting the probable problems within which each of these two strategies (classification and clustering) can be put into place within the mining process of an ERP system.

Dynamic Data Mining

2011 ◽  
pp. 722-728 ◽  
Author(s):  
Richard Weber
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Credit Card ◽  
Knowledge Discovery In Databases ◽  
Stream Data ◽  
Dynamic Data ◽  
Change Over Time ◽  
Professional Activities ◽  
Feature Values ◽  
Over Time

Since the First KDD Workshop back in 1989 when “Knowledge Mining” was recognized as one of the top 5 topics in future database research (Piatetsky-Shapiro 1991), many scientists as well as users in industry and public organizations have considered data mining as highly relevant for their respective professional activities. We have witnessed the development of advanced data mining techniques as well as the successful implementation of knowledge discovery systems in many companies and organizations worldwide. Most of these implementations are static in the sense that they do not contemplate explicitly a changing environment. However, since most analyzed phenomena change over time, the respective systems should be adapted to the new environment in order to provide useful and reliable analyses. If we consider for example a system for credit card fraud detection, we may want to segment our customers, process stream data generated by their transactions, and finally classify them according to their fraud probability where fraud pattern change over time. If our segmentation should group together homogeneous customers using not only their current feature values but also their trajectories, things get even more difficult since we have to cluster vectors of functions instead of vectors of real values. An example for such a trajectory could be the development of our customers’ number of transactions over the past six months or so if such a development tells us more about their behavior than just a single value; e.g., the most recent number of transactions. It is in this kind of applications is where dynamic data mining comes into play! Since data mining is just one step of the iterative KDD (Knowledge Discovery in Databases) process (Han & Kamber, 2001), dynamic elements should be considered also during the other steps. The entire process consists basically of activities that are performed before doing data mining (such as: selection, pre-processing, transformation of data (Famili et al., 1997)), the actual data mining part, and subsequent steps (such as: interpretation, evaluation of results). In subsequent sections we will present the background regarding dynamic data mining by studying existing methodological approaches as well as already performed applications and even patents and tools. Then we will provide the main focus of this chapter by presenting dynamic approaches for each step of the KDD process. Some methodological aspects regarding dynamic data mining will be presented in more detail. After envisioning future trends regarding dynamic data mining we will conclude this chapter.

scholarly journals GRAPH-BASED HIERARCHICAL CONCEPTUAL CLUSTERING

2001 ◽  
Vol 10 (01n02) ◽  
pp. 107-135 ◽  
Author(s):  
ISTVAN JONYER ◽  
LAWRENCE B. HOLDER ◽  
DIANE J. COOK
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Structural Information ◽  
Clustering Algorithms ◽  
Unstructured Data ◽  
Conceptual Clustering ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Discovery System

Hierarchical conceptual clustering has proven to be a useful, although greatly under-explored data mining technique. A graph-based representation of structural information combined with a substructure discovery technique has been shown to be successful in knowledge discovery. The SUBDUE substructure discovery system provides the advantages of both approaches. This work presents SUBDUE and the development of its clustering functionalities. Several examples are used to illustrate the validity of the approach both in structured and unstructured domains, as well as compare SUBDUE to earlier clustering algorithms. Results show that SUBDUE successfully discovers hierarchical clusterings in both structured and unstructured data.

Mining Indirect Temporal Sequential Patterns in Large Transaction Databases

2013 ◽  
Vol 385-386 ◽  
pp. 1362-1365
Author(s):  
Wei Min Ouyang ◽  
Qin Hua Huang
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Research Topic ◽  
Sequential Pattern ◽  
The Other ◽  
Sequential Patterns ◽  
Important Research ◽  
Data Set ◽  
Important Research Topic ◽  
The One

Sequential pattern is an important research topic in data mining and knowledge discovery. Traditional algorithms for mining sequential patterns focus on the frequent sequences, which do not consider the infrequent sequences and lifespan of each sequence. On the one hand, some infrequent patterns can provide very useful insight view into the data set, on the other hand, without taking lifespan of each sequence into account, not only some discovered patterns may be invalid, but also some useful patterns may not be discovered. So, we extend the sequential patterns to the indirect temporal sequential patterns, and put forward an algorithm to discover indirect temporal sequential patterns in this paper.

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