Search
Search Results
-
Mining Discriminative Itemsets Over Data Streams Using Efficient Sliding Window
In this paper, we present an efficient novel method for mining discriminative itemsets over data streams using the sliding window model....
-
Frequent Pattern
Frequent patterns can be used to characterize a given set of examples: they are the most typical feature combinations in the data. Frequent patterns...
-
GrAFCI+ A fast generator-based algorithm for mining frequent closed itemsets
Mining itemsets for association rule generation is a fundamental data mining task originally stemming from the traditional market basket analysis...
-
An innovative clustering approach utilizing frequent item sets
Clustering is a method in data mining that belongs to the category of unsupervised learning. Cluster analysis categorizes data into different classes...
-
Mining discriminative itemsets in data streams using the tilted-time window model
A discriminative itemset is a frequent itemset in the target data stream with much higher frequency than that of the same itemset in the rest of the...
-
MFS-SubSC: an efficient algorithm for mining frequent sequences with sub-sequence constraint
Mining frequent sequences (FS) with constraints in a sequence database (SDB) are a critical task in Data Mining, as it forms the basis for...
-
Binary image description using frequent itemsets
In this paper, a novel method for binary image comparison is presented. We suppose that the image is a set of transactions and items. The proposed...
-
DAC: Discriminative Associative Classification
In this paper, discriminative associative classification is proposed as a new classification technique based on class discriminative association...
-
Frequent itemset hiding revisited: pushing hiding constraints into mining
This paper introduces a new theoretical scheme for the solution of the frequent itemset hiding problem. We propose an algorithmic approach that...
-
A Constraint-Based Model for the Frequent Itemset Hiding Problem
This paper introduces a novel constraint-based hiding model to drastically reduce the preprocessing overhead that is incurred by border-based...
-
Hiding sensitive itemsets without side effects
Data mining techniques are being used to discover useful patterns hidden in the data. However, these data mining techniques also extract sensitive...
-
Incremental Mining on Association Rules
The discovery of association rules has been known to be useful in selective marketing, decision analysis, and business management. An important...
-
Efficiently Mining Closed Interval Patterns with Constraint Programming
Constraint programming (CP) has become increasingly prevalent in recent years for performing pattern mining tasks, particularly on binary datasets....
-
GridWall: A Novel Condensed Representation of Frequent Itemsets
A complete set of frequent itemset can be extremely and unexpectedly large due to redundancy when the given minimum support is low or when the...
-
An incremental framework to extract coverage patterns for dynamic databases
Pattern mining is an important task of data mining and involves the extraction of interesting associations from large transactional databases....
-
Sequential Pattern Mining by Pattern-Growth: Principles and Extensions*
Sequential pattern mining is an important data mining problem with broad applications. However, it is also a challenging problem since the mining may...
-
Machine Learning Algorithms
This chapter introduces the different types of algorithms that are used in machine learning to perform different operations. The chapter begins by...
-
A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and Challenges
Supervised classification based on Contrast Patterns (CP) is a trending topic in the pattern recognition literature, partly because it contains an...
-
Rare association rule mining from incremental databases
Rare association rule mining is an imperative field of data mining that attempts to identify rare correlations among the items in a database....
