Mining Frequent and Rare Itemsets With Weighted Supports Using Additive Neural Itemset Embedding

2021 ◽  
Author(s):  
Yi Ji ◽  
Yukio Ohsawa
Keyword(s):  
Rare Itemsets

scholarly journals Finding Efficient Positive and Negative Itemsets Using Interestingness Measures

2018 ◽  
Vol 7 (4.36) ◽  
pp. 533
Author(s):  
P. Asha ◽  
T. Prem Jacob ◽  
A. Pravin
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Data Gathering ◽  
Unstructured Data ◽  
Rule Mining ◽  
Interestingness Measures ◽  
Data Mining Algorithms ◽  
Data Formats ◽  
Mining Algorithms ◽  
Rare Itemsets

Currently, data gathering techniques have increased through which unstructured data creeps in, along with well defined data formats. Mining these data and bringing out useful patterns seems difficult. Various data mining algorithms were put forth for this purpose. The associated patterns generated by the association rule mining algorithms are large in number. Every ARM focuses on positive rule mining and very few literature has focussed on rare_itemsets_mining. The work aims at retrieving the rare itemsets that are of most interest to the user by utilizing various interestingness measures. Both positive and negative itemset mining would be focused in this work.  

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.

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.

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.

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