Exploration of Soft Computing Approaches in Itemset Mining

2014 ◽  
pp. 270-296
Author(s):  
Jyothi Pillai ◽  
O. P. Vyas
Keyword(s):  
Data Mining ◽  
Soft Computing ◽  
Low Cost ◽  
Itemset Mining ◽  
Huge Data ◽  
Large Databases ◽  
Soft Computing Techniques ◽  
Good Learning ◽  
Priority Criterion ◽  
Rare Itemsets

Data Mining is largely known to extract knowledge from large databases in an attempt to discover existing trends and newer patterns. While data mining refers to information extraction, soft computing is more inclined to information processing. Using Soft Computing, the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth for achieving tractability, robustness, and low-cost solutions can be revealed. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Soft computing techniques are Fuzzy Logic (FL), Neural Network (NN), Genetic Algorithm (GA), Rough Set (RS), etc. For handling different types of uncertainty in huge data, FL and RS are highly suitable. NNs are a nonparametric, robust technique and provide good learning and generalization capabilities in data-rich environments. GAs provide efficient search algorithms for selecting a model, from mixed-media data, based on some priority criterion. In one of its realms, Association Rule Mining (ARM) and Itemset mining have been a focus of research in data mining for a decade, including finding most frequent item sets and corresponding association rules and extracting rare itemsets including temporal and fuzzy concepts in discovered patterns. The objective of this chapter is to explore the usage of Soft Computing approaches in itemset utility mining, both frequent and rare itemsets. In addition, a literature review of applications of soft computing techniques in temporal mining is described.

Exploration of Soft Computing Approaches in Itemset Mining

2016 ◽  
pp. 1830-1856
Author(s):  
Jyothi Pillai ◽  
O. P. Vyas
Keyword(s):  
Data Mining ◽  
Soft Computing ◽  
Low Cost ◽  
Itemset Mining ◽  
Huge Data ◽  
Large Databases ◽  
Soft Computing Techniques ◽  
Good Learning ◽  
Priority Criterion ◽  
Rare Itemsets

Data Mining is largely known to extract knowledge from large databases in an attempt to discover existing trends and newer patterns. While data mining refers to information extraction, soft computing is more inclined to information processing. Using Soft Computing, the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth for achieving tractability, robustness, and low-cost solutions can be revealed. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Soft computing techniques are Fuzzy Logic (FL), Neural Network (NN), Genetic Algorithm (GA), Rough Set (RS), etc. For handling different types of uncertainty in huge data, FL and RS are highly suitable. NNs are a nonparametric, robust technique and provide good learning and generalization capabilities in data-rich environments. GAs provide efficient search algorithms for selecting a model, from mixed-media data, based on some priority criterion. In one of its realms, Association Rule Mining (ARM) and Itemset mining have been a focus of research in data mining for a decade, including finding most frequent item sets and corresponding association rules and extracting rare itemsets including temporal and fuzzy concepts in discovered patterns. The objective of this chapter is to explore the usage of Soft Computing approaches in itemset utility mining, both frequent and rare itemsets. In addition, a literature review of applications of soft computing techniques in temporal mining is described.

scholarly journals Modern Applications and Challenges for Rare Itemset Mining

2021 ◽  
Vol 11 (3) ◽  
pp. 208-218
Author(s):  
Sadeq Darrab ◽  
◽  
David Broneske ◽  
Gunter Saake
Keyword(s):  
Data Mining ◽  
Real Life ◽  
The State ◽  
Frequent Itemset ◽  
Future Research ◽  
Comprehensive Overview ◽  
Itemset Mining ◽  
Equipment Failures ◽  
Mining Algorithms ◽  
Rare Itemsets

Data mining is the process of extracting useful unknown knowledge from large datasets. Frequent itemset mining is the fundamental task of data mining that aims at discovering interesting itemsets that frequently appear together in a dataset. However, mining infrequent (rare) itemsets may be more interesting in many real-life applications such as predicting telecommunication equipment failures, genetics, medical diagnosis, or anomaly detection. In this paper, we survey up-to-date methods of rare itemset mining. The main goal of this survey is to provide a comprehensive overview of the state-of-the-art algorithms of rare itemset mining and its applications. The main contributions of this survey can be summarized as follows. In the first part, we define the task of rare itemset mining by explaining key concepts and terminology, motivation examples, and comparisons with underlying concepts. Then, we highlight the state-of-art methods for rare itemsets mining. Furthermore, we present variations of the task of rare itemset mining to discuss limitations of traditional rare itemset mining algorithms. After that, we highlight the fundamental applications of rare itemset mining. In the last, we point out research opportunities and challenges for rare itemset mining for future research.

Observing Customer Segment Stability Using Soft Computing Techniques and Markov Chains Within Data Mining Framework

2018 ◽  
pp. 1440-1457
Author(s):  
Abdulkadir Hiziroglu
Keyword(s):  
Data Mining ◽  
Markov Chains ◽  
Soft Computing ◽  
Internal Validity ◽  
Real World Data ◽  
Soft Computing Techniques ◽  
Customer Segment ◽  
Managerial Implications ◽  
Classification Prediction ◽  
The Stability

This study proposes a model that utilizes soft computing and Markov Chains within a data mining framework to observe the stability of customer segments. The segmentation process in this study includes clustering of existing consumers and classification-prediction of segments for existing and new customers. Both a combination and an integration of soft computing techniques were used in the proposed model. Segmenting customers was done according to the purchasing behaviours of customers based on RFM (Recency, Frequency, Monetary) values. The model was applied to real-world data that were procured from a UK retail chain covering four periods of shopping transactions of around 300,000 customers. Internal validity was measured by two different clustering validity indices and a classification accuracy test. Some meaningful information associated with segment stability was extracted to provide practitioners a better understanding of segment stability over time and useful managerial implications.

scholarly journals Performance Research on Medical Data Classification using Traditional and Soft Computing Techniques

2019 ◽  
Vol 8 (2S3) ◽  
pp. 990-995
Keyword(s):  
Data Mining ◽  
Soft Computing ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Medical Data ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Classification Techniques ◽  
Soft Computing Techniques

The world today has made giant leaps in the field of Medicine. There is tremendous amount of researches being carried out in this field leading to new discoveries that is making a heavy impact on the mankind. Data being generated in this field is increasing enormously. A need has arisen to analyze these data in order to find out the meaningful and relevant hidden patterns. These patterns can be used for clinical diagnosis. Data mining is an efficient approach in discovering these patterns. Among the many data mining techniques that exists, this paper aims at analyzing the medical data using various Classification techniques. The classification techniques used in this study include k-Nearest neighbor (kNN), Decision Tree, Naive Bayes which are hard computing algorithms, whereas the soft computing algorithms used in this study include Support Vector Machine (SVM), Artificial Neural Networks (ANN) and Fuzzy k-Means clustering. We have applied these algorithms to three kinds of datasets that are Breast Cancer Wisconsin, Haberman Data and Contraceptive Method Choice dataset. Our results show that soft computing based classification algorithms better classifications than the traditional classification algorithms in terms of various classification performance measures

Observing Customer Segment Stability Using Soft Computing Techniques and Markov Chains within Data Mining Framework

2015 ◽  
Vol 6 (1) ◽  
pp. 59-75 ◽  
Author(s):  
Abdulkadir Hiziroglu
Keyword(s):  
Data Mining ◽  
Markov Chains ◽  
Soft Computing ◽  
Internal Validity ◽  
Real World Data ◽  
Validity Indices ◽  
Soft Computing Techniques ◽  
Customer Segment ◽  
Managerial Implications ◽  
Classification Prediction

This study proposes a model that utilizes soft computing and Markov Chains within a data mining framework to observe the stability of customer segments. The segmentation process in this study includes clustering of existing consumers and classification-prediction of segments for existing and new customers. Both a combination and an integration of soft computing techniques were used in the proposed model. Segmenting customers was done according to the purchasing behaviours of customers based on RFM (Recency, Frequency, Monetary) values. The model was applied to real-world data that were procured from a UK retail chain covering four periods of shopping transactions of around 300,000 customers. Internal validity was measured by two different clustering validity indices and a classification accuracy test. Some meaningful information associated with segment stability was extracted to provide practitioners a better understanding of segment stability over time and useful managerial implications.

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