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Mining Approximate Frequent Itemsets In the Presence of Noise: Algorithm and Analysis [chapter]

Jinze Liu, Susan Paulsen, Xing Sun, Wei Wang, Andrew Nobel, Jan Prins
2006 Proceedings of the 2006 SIAM International Conference on Data Mining  
In this paper we propose a noise tolerant itemset model, which we call approximate frequent itemsets (AFI).  ...  We developed and implemented an algorithm to mine AFIs that generalizes the level-wise enumeration of frequent itemsets by allowing noise.  ...  The failure of classical frequent itemset mining to detect simple patterns in the presence of random errors compromises the ability of these algorithms to detect associations, cluster items, or build classifiers  ... 
doi:10.1137/1.9781611972764.36 dblp:conf/sdm/LiuPSWNP06 fatcat:4aihlbmdczburbvktmvbamelri

MEDICAL DECISION SUPPORT SYSTEM OF COMMON CAROTID ARTERY USING IMAGE MINING AND DUAL SNAKE SEGMENTATION PROCEDURE

L Ashok Kumar, P Rajendran
2017 International Research Journal of Pharmacy  
The dual snake segmentation procedure and pruned association rule with improved Apriori algorithm has been used in this paper to develop a common carotid image classification.  ...  The low level features extracted from the ultrasound common carotid artery images and high level knowledge from specialists were used to enhance the accuracy in decision process.  ...  Iteratively find frequent itemsets with cardinality from 1 to k (k-itemset). Use the frequent itemsets to generate association rules.  ... 
doi:10.7897/2230-8407.08578 fatcat:d4wbcrbgarhe7glwyqnossqkla

Hybrid Medical Image Classification Using Association Rule Mining with Decision Tree Algorithm [article]

P. Rajendran, M.Madheswaran
2010 arXiv   pre-print
The pre-processing step has been done using the median filtering process and edge features have been extracted using canny edge detection technique.  ...  The frequent patterns from the CT scan images are generated by frequent pattern tree (FP-Tree) algorithm that mines the association rules.  ...  The definition 4.1 can be used in the situation where maximum frequent itemsets are short.  ... 
arXiv:1001.3503v1 fatcat:v4qbxgpn7ja5hbhjzynvkbr4m4

Mining Rare Patterns by Using Automated Threshold Support

Prof. Mangesh Ghonge, Miss Neha Rane
2018 International Journal of Engineering & Technology  
In frequent itemset, those things which occurs frequently whereas, in infrequent itemset the items that occur very rarely are obtained.  ...  Determining such form of data is tougher than to locate data which occurs frequently. Frequent Itemset Mining (FISM) locates large and frequent itemsets in huge data for example market baskets.  ...  Concept of utility itemset hails from frequent itemset mining. [3] proposes an algorithm using top-k algorithm for mining closed high utility itemset.  ... 
doi:10.14419/ijet.v7i3.8.15225 fatcat:svkt345yvbgj7dh5bvxeaeiqwu

A Scalable and Efficient Outlier Detection Strategy for Categorical Data

Anna Koufakou, Enrique G. Ortiz, Michael Georgiopoulos, Georgios C. Anagnostopoulos, Kenneth M. Reynolds
2007 19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007)  
Outlier detection has received significant attention in many applications, such as detecting credit card fraud or network intrusions.  ...  AVF is compared with a list of representative outlier detection approaches that have not been contrasted against each other.  ...  [15] also deal with outliers in categorical datasets and use frequent itemsets, as well: they use hyperedges, which simply store frequent itemsets along with the data points that contain these frequent  ... 
doi:10.1109/ictai.2007.125 dblp:conf/ictai/KoufakouOGAR07 fatcat:cauqplj4zvdtrar2pa6anwji5y

Connections between Mining Frequent Itemsets and Learning Generative Models

Srivatsan Laxman, Prasad Naldurg, Raja Sripada, Ramarathnam Venkatesan
2007 Seventh IEEE International Conference on Data Mining (ICDM 2007)  
We present a class of models called Itemset Generating Models (or IGMs) that can be used to formally connect the process of frequent itemsets discovery with the learning of generative models.  ...  Frequent itemsets mining is a popular framework for pattern discovery.  ...  This makes IGMs a reasonable class of models to use for connections with frequent itemset mining. We formalize these ideas in the subsections to follow.  ... 
doi:10.1109/icdm.2007.83 dblp:conf/icdm/LaxmanNSV07 fatcat:koewqvfohzhd7hv47cjvpqhduq

LODE: A distance-based classifier built on ensembles of positive and negative observations

Rosa Meo, Dipankar Bachar, Dino Ienco
2012 Pattern Recognition  
Furthermore, since absent features are frequent features in their respective classes, they make the prediction more robust against over-fitting and noise.  ...  Typical features are modelled by frequent itemsets extracted from the examples and constitute a new representation space of the examples of the class.  ...  We used as classification patterns the frequent set of items (called itemsets). Itemsets are introduced in Subsection 4.1 [18] .  ... 
doi:10.1016/j.patcog.2011.10.015 fatcat:cp24hwc5efd7bd3k4fqmr4whbi

A Survey on Mining Frequent Itemsets over Data Streams

Shailvi Maurya, Sneha Ambhore, Sneha Parit
2017 International Journal of Computer Applications  
Mining frequent itemsets over data stream has been challenging task.  ...  Apriori based techniques, Frequent Pattern growth (FPgrowth) and Equivalence CLASS Transformation (ECLAT) are the approaches used mostly in extracting frequent patterns.  ...  In data mining the main focus is on cleansing of data which is known as noise elimination or noise reduction.  ... 
doi:10.5120/ijca2017916030 fatcat:ojkfobsynnedhhp7d7ayxabo7a

Efficiently Mining Interesting Emerging Patterns [chapter]

Hongjian Fan, Kotagiri Ramamohanarao
2003 Lecture Notes in Computer Science  
How to efficiently discover the complete set of Emerging Patterns between two classes of data? 2.  ...  Real-world classification problems always contain noise. A reliable classifier should be tolerant to a reasonable level of noise.  ...  Although influenced by LB, BCEP is different from LB. • First, BCEP uses EPs, which are itemsets frequent in one data class while very infrequent in another. In contrast, LB uses frequent itemsets.  ... 
doi:10.1007/978-3-540-45160-0_19 fatcat:5dx6cgvn6jeb7pxzckxsc3ov3e

Efficient Mining of Frequent and Distinctive Feature Configurations

Till Quack, Vittorio Ferrari, Bastian Leibe, Luc Van Gool
2007 2007 IEEE 11th International Conference on Computer Vision  
We present a novel approach to automatically find spatial configurations of local features occurring frequently on instances of a given object class, and rarely on the background.  ...  intermediate processing layer to filter the large amount of clutter features returned by lowlevel feature extraction, and hence to facilitate the tasks of higher-level processing stages such as object detection  ...  Acknowledgments We acknowledge support from EU project CLASS, IST 027978 and Swiss NSF project IM2.  ... 
doi:10.1109/iccv.2007.4408906 dblp:conf/iccv/QuackFLG07 fatcat:jdbbwfjdvrdsxc5dskx2ahvolu

Emergent Semantic Patterns in Large Scale Image Dataset: A Datamining Approach

Umair Mateen Khan, Brendan McCane, Andrew Trotman
2012 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA)  
These bags of words are then used for mining co-occurring patterns.  ...  Initially, local image features are extracted using image processing techniques which are then clustered to generate a bag of words (BoW) for each image.  ...  To extract these features, techniques such as edge detection, corner detection [22] , blob [23] and ridge detection [24] etc. are used.  ... 
doi:10.1109/dicta.2012.6411739 dblp:conf/dicta/KhanMT12 fatcat:xtsarwsegbehdbfmz5py4b3nyq

Logical Itemset Mining

Shailesh Kumar, Chandrashekar V., C.V. Jawahar
2012 2012 IEEE 12th International Conference on Data Mining Workshops  
Frequent Itemset Mining (FISM) attempts to find large and frequent itemsets in bag-of-items data such as retail market baskets.  ...  We conclude that while FISM discovers a large number of noisy, observed, and frequent itemsets, LISM discovers a small number of high quality, latent logical itemsets.  ...  For example, we also restrict our model to pair-wise relationships only, but we use a different class of much simpler and noise-robust measures of associations.  ... 
doi:10.1109/icdmw.2012.85 dblp:conf/icdm/KumarVJ12 fatcat:nr3dbiknuzgvnfxvpztqvsbjpu

Frequent Itemset Based Hierarchical Document Clustering Using Wikipedia as External Knowledge [chapter]

Kiran G.V.R., Ravi Shankar, Vikram Pudi
2010 Lecture Notes in Computer Science  
We propose a hierarchical clustering algorithm using closed frequent itemsets that use Wikipedia as an external knowledge to enhance the document representation.  ...  Some of the recent algorithms address this problem by using frequent itemsets for clustering. But, most of these algorithms neglect the semantic relationship between the words.  ...  Then, in this doc-space, frequent combinations of keywords (i.e., frequent itemsets) Table 1 . 1 Document Clustering and Topic Detection Frequent Itemset Mining Document Clustering Topic Detection  ... 
doi:10.1007/978-3-642-15390-7_2 fatcat:nadxp6mh75az5kjtemoagwxbgq

Pattern discovery for object categorization

Edmond Zhang, Michael Mayo
2008 2008 23rd International Conference Image and Vision Computing New Zealand  
Instead, our model attempts to discover intermediate representations for each object class.  ...  If there exist a frequent itemset for that keypoint, increase the weight counter on that frequent itemset. 4.  ...  Traverse through one keypoint at a time, generating a new frequent itemset for that keypoint if there is no existing frequent itemset for that keypoint 3.  ... 
doi:10.1109/ivcnz.2008.4762071 fatcat:fjby7dnxxvf6tizohrw2jkizay

Compositional object pattern

Shen-Fu Tsai, Liangliang Cao, Feng Tang, Thomas S. Huang
2011 Proceedings of the 19th ACM international conference on Multimedia - MM '11  
To interpret the rich semantics in albums, we mine frequent object patterns in the training set, and then rank them by their discriminating power.  ...  The album feature is then set as the frequencies of these frequent and discriminative patterns, called Compositional Object Pattern Frequency(COPF).  ...  When mining frequent closed itemsets, we use the program of CLOSET+ [13] provided by its authors, with threshold of support set to 30.  ... 
doi:10.1145/2072298.2072015 dblp:conf/mm/TsaiCTH11 fatcat:jnoowfegnndwtngdlfg2fivome
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