scholarly journals A study on sequential pattern mining on chemical information

2018 ◽  
Vol 7 (3.3) ◽  
pp. 532
Author(s):  
S Sathya ◽  
N Rajendran
Keyword(s):  
Data Mining ◽  
Chemical Bonding ◽  
Sequential Analysis ◽  
Pattern Mining ◽  
Fundamental Problem ◽  
Research Work ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Graph Representation ◽  
Chemical Information

Data mining (DM) is used for extracting the useful and non-trivial information from the large amount of data to collect in many and diverse fields. Data mining determines explanation through clustering visualization, association and sequential analysis. Chemical compounds are well-defined structures compressed by a graph representation. Chemical bonding is the association of atoms into molecules, ions, crystals and other stable species which frame the common substances in chemical information. However, large-scale sequential data is a fundamental problem like higher classification time and bonding time in data mining with many applications. In this work, chemical structured index bonding is used for sequential pattern mining. Our research work helps to evaluate the structural patterns of chemical bonding in chemical information data sets.  

scholarly journals A Framework for Data Mining Pattern Management

2004 ◽  
pp. 87-98 ◽  
Author(s):  
Barbara Catania ◽  
Anna Maddalena ◽  
Maurizio Mazza ◽  
Elisa Bertino ◽  
Stefano Rizzi
Keyword(s):  
Data Mining ◽  
Mining Pattern

Applications of Pattern Discovery Using Sequential Data Mining

2012 ◽  
pp. 1-23 ◽  
Author(s):  
Manish Gupta ◽  
Jiawei Han
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Intrusion Detection ◽  
Pattern Mining ◽  
Pattern Discovery ◽  
Sequential Pattern Mining ◽  
Web Usage Mining ◽  
Sequential Pattern ◽  
Sequential Data ◽  
Mining Methods

Sequential pattern mining methods have been found to be applicable in a large number of domains. Sequential data is omnipresent. Sequential pattern mining methods have been used to analyze this data and identify patterns. Such patterns have been used to implement efficient systems that can recommend based on previously observed patterns, help in making predictions, improve usability of systems, detect events, and in general help in making strategic product decisions. In this chapter, we discuss the applications of sequential data mining in a variety of domains like healthcare, education, Web usage mining, text mining, bioinformatics, telecommunications, intrusion detection, et cetera. We conclude with a summary of the work.

Clustering of Time Series Data

2011 ◽  
pp. 258-263
Author(s):  
Anne Denton
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Pattern Mining ◽  
Time Series Data ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Series Data ◽  
Science And Engineering ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Time series data is of interest to most science and engineering disciplines and analysis techniques have been developed for hundreds of years. There have, however, in recent years been new developments in data mining techniques, such as frequent pattern mining, that take a different perspective of data. Traditional techniques were not meant for such pattern-oriented approaches. There is, as a result, a significant need for research that extends traditional time-series analysis, in particular clustering, to the requirements of the new data mining algorithms.

Domain Driven Data Mining

2008 ◽  
pp. 196-223 ◽  
Author(s):  
Longbing Cao ◽  
Chengqi Zhang
Keyword(s):  
Data Mining ◽  
Complex Systems ◽  
Real World ◽  
Domain Knowledge ◽  
Pattern Mining ◽  
Iterative Refinement ◽  
User Preference ◽  
Data Driven ◽  
Real World Data ◽  
Hidden Knowledge

Quantitative intelligence based traditional data mining is facing grand challenges from real-world enterprise and cross-organization applications. For instance, the usual demonstration of specific algorithms cannot support business users to take actions to their advantage and needs. We think this is due to Quantitative Intelligence focused data-driven philosophy. It either views data mining as an autonomous data-driven, trial-and-error process, or only analyzes business issues in an isolated, case-by-case manner. Based on experience and lessons learnt from real-world data mining and complex systems, this article proposes a practical data mining methodology referred to as Domain-Driven Data Mining. On top of quantitative intelligence and hidden knowledge in data, domain-driven data mining aims to meta-synthesize quantitative intelligence and qualitative intelligence in mining complex applications in which human is in the loop. It targets actionable knowledge discovery in constrained environment for satisfying user preference. Domain-driven methodology consists of key components including understanding constrained environment, business-technical questionnaire, representing and involving domain knowledge, human-mining cooperation and interaction, constructing next-generation mining infrastructure, in-depth pattern mining and postprocessing, business interestingness and actionability enhancement, and loop-closed human-cooperated iterative refinement. Domain-driven data mining complements the data-driven methodology, the metasynthesis of qualitative intelligence and quantitative intelligence has potential to discover knowledge from complex systems, and enhance knowledge actionability for practical use by industry and business.

Pattern Based Feature Construction in Semantic Data Mining

2014 ◽  
Vol 10 (1) ◽  
pp. 27-65 ◽  
Author(s):  
Agnieszka Ławrynowicz ◽  
Jędrzej Potoniec
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Semantic Features ◽  
Semantic Data ◽  
Data Mining Approach ◽  
Meta Learning ◽  
New Type ◽  
Domain Ontologies ◽  
Semantic Data Mining

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.

Research of Data Graph Mining Based on Telecommunication Customers

2013 ◽  
Vol 443 ◽  
pp. 402-406 ◽  
Author(s):  
Shang Gao ◽  
Mei Mei Li
Keyword(s):  
Data Mining ◽  
Graph Mining ◽  
Pattern Mining ◽  
Rapid Development ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Practical Significance ◽  
Research Progress ◽  
Graph Data ◽  
Data Graph

With the rapid development of the number of mobile phone users has accumulated a large number of graph data, graph data mining has gradually become a hot area of research. Traditional data such as clustering, classification, frequent pattern mining gradually extended to the field of graph data mining research. Introduced at this stage graph data mining technology research progress, summarizes the characteristics of the graphical data mining, practical significance, the main problem, and scenarios to discuss and forecast chart data, especially research on uncertain graph data become trends and hot spots.

scholarly journals BIG DATA MINING FOR INTERESTING PATTERNS WITH MAP REDUCE TECHNIQUE

2017 ◽  
Vol 10 (13) ◽  
pp. 191
Author(s):  
Nikhil Jamdar ◽  
A Vijayalakshmi
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Uncertain Data ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Map Reduce ◽  
Frequent Patterns ◽  
Precise Data ◽  
Big Data Mining ◽  
Transactional Databases

There are many algorithms available in data mining to search interesting patterns from transactional databases of precise data. Frequent pattern mining is a technique to find the frequently occurred items in data mining. Most of the techniques used to find all the interesting patterns from a collection of precise data, where items occurred in each transaction are certainly known to the system. As well as in many real-time applications, users are interested in a tiny portion of large frequent patterns. So the proposed user constrained mining approach, will help to find frequent patterns in which user is interested. This approach will efficiently find user interested frequent patterns by applying user constraints on the collections of uncertain data. The user can specify their own interest in the form of constraints and uses the Map Reduce model to find uncertain frequent pattern that satisfy the user-specified constraints 

Multi-Relational Sequential Pattern Mining Based on Iceberg Concept Lattice

2011 ◽  
Vol 109 ◽  
pp. 729-733
Author(s):  
Jiang Yin ◽  
Yun Li ◽  
Cen Cheng Shen ◽  
Bo Liu
Keyword(s):  
Data Mining ◽  
Time Complexity ◽  
Pattern Mining ◽  
Concept Lattice ◽  
Optimization Methods ◽  
Sequential Pattern Mining ◽  
Experimental Results ◽  
Sequential Pattern ◽  
Sequential Mining ◽  
Mining Methods

Multi-Relational Sequential mining is one of the areas of data mining that rapidly developed in recent years. However, the performance issues of traditional mining methods are not ideal. To effectively mining the pattern, we proposed an algorithm based on Iceberg concept lattice, adopting optimization methods of partition and merger to just mining the frequent sequences. Experimental results show this algorithm effectively reduced the time complexity of multi-relational sequential pattern mining.

scholarly journals Interactive Learning of Pattern Rankings

2014 ◽  
Vol 23 (06) ◽  
pp. 1460026 ◽  
Author(s):  
Vladimir Dzyuba ◽  
Matthijs van Leeuwen ◽  
Siegfried Nijssen ◽  
Luc De Raedt
Keyword(s):  
Data Mining ◽  
Active Learning ◽  
Pattern Mining ◽  
Interactive Learning ◽  
Building Blocks ◽  
Frequent Itemset ◽  
Preference Learning ◽  
Ranking Functions ◽  
Learning Techniques ◽  
Learning Heuristics

Pattern mining provides useful tools for exploratory data analysis. Numerous efficient algorithms exist that are able to discover various types of patterns in large datasets. Unfortunately, the problem of identifying patterns that are genuinely interesting to a particular user remains challenging. Current approaches generally require considerable data mining expertise or effort from the data analyst, and hence cannot be used by typical domain experts. To address this, we introduce a generic framework for interactive learning of userspecific pattern ranking functions. The user is only asked to rank small sets of patterns, while a ranking function is inferred from this feedback by preference learning techniques. Moreover, we propose a number of active learning heuristics to minimize the effort required from the user, while ensuring that accurate rankings are obtained. We show how the learned ranking functions can be used to mine new, more interesting patterns. We demonstrate two concrete instances of our framework for two different pattern mining tasks, frequent itemset mining and subgroup discovery. We empirically evaluate the capacity of the algorithm to learn pattern rankings by emulating users. Experiments demonstrate that the system is able to learn accurate rankings, and that the active learning heuristics help reduce the required user effort. Furthermore, using the learned ranking functions as search heuristics allows discovering patterns of higher quality than those in the initial set. This shows that machine learning techniques in general, and active preference learning in particular, are promising building blocks for interactive data mining systems.

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