A Clustering based Prediction Scheme for High Utility Itemsets

We strongly believe that the current Utility Itemset Mining (UIM) problem model can be extended with a key modeling capability of predicting future itemsets based on prior knowledge of clusters in the dataset. Information in transactions fairly representative of a cluster type is more a characteristic of the cluster type than the the entire data. Subjecting such transactions to the common threshold in the UIM problem leads to information loss. We identify that an implicit use of the cluster structure of data in the UIM problem model will address this limitation. We achieve this by introducing a new clustering based utility in the definition of the UIM problem model and modifying the definitions of absolute utilities based on it. This enhances the UIM model by including a predictive aspect to it, thereby enabling the cluster specific patterns to emerge while still mining the inter-cluster patterns. By performing experiments on two real data sets we are able to verify that our proposed predictive UIM problem model extracts more useful information than the current UIM model with high accuracy

A Novel Clustering Algorithm to Capture Utility Information in Transactional Data

We develop and design a novel clustering algorithm to capture utility information in transactional data. Transactional data is a special type of categorical data where transactions can be of varying length. A key objective for all categorical data analysis is pattern recognition. Therefore, transactional clustering algorithms focus on capturing the information on high frequency patterns from the data in the clusters. In recent times, utility information for category types in the data has been added to the transactional data model for a more realistic representation of data. As a result, the key information of interest has become high utility patterns instead of high frequency patterns. To the best our knowledge, no existing clustering algorithm for transactional data captures the utility information in the clusters found. Along with our new clustering rationale we also develop corresponding metrics for evaluating quality of clusters found. Experiments on real datasets show that the clusters found by our algorithm successfully capture the high utility patterns in the data. Comparative experiments with other clustering algorithms further illustrate the effectiveness of our algorithm

A Clustering based Prediction Scheme for High Utility Itemsets

We strongly believe that the current Utility Itemset Mining (UIM) problem model can be extended with a key modeling capability of predicting future itemsets based on prior knowledge of clusters in the dataset. Information in transactions fairly representative of a cluster type is more a characteristic of the cluster type than the the entire data. Subjecting such transactions to the common threshold in the UIM problem leads to information loss. We identify that an implicit use of the cluster structure of data in the UIM problem model will address this limitation. We achieve this by introducing a new clustering based utility in the definition of the UIM problem model and modifying the definitions of absolute utilities based on it. This enhances the UIM model by including a predictive aspect to it, thereby enabling the cluster specific patterns to emerge while still mining the inter-cluster patterns. By performing experiments on two real data sets we are able to verify that our proposed predictive UIM problem model extracts more useful information than the current UIM model with high accuracy.This proceeding is published as Piyush Lakhawat and Arun K. Somani, “A Clustering based Prediction Scheme for High Utility Itemsets.” In: Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, 123-134, 2017, Funchal, Madeira, Portugal. DOI: 10.5220/0006590001230134. Published in SCITEPRESS Digital Library. Posted with permission.</p

A Novel Clustering Algorithm to Capture Utility Information in Transactional Data

We develop and design a novel clustering algorithm to capture utility information in transactional data. Transactional data is a special type of categorical data where transactions can be of varying length. A key objective for all categorical data analysis is pattern recognition. Therefore, transactional clustering algorithms focus on capturing the information on high frequency patterns from the data in the clusters. In recent times, utility information for category types in the data has been added to the transactional data model for a more realistic representation of data. As a result, the key information of interest has become high utility patterns instead of high frequency patterns. To the best our knowledge, no existing clustering algorithm for transactional data captures the utility information in the clusters found. Along with our new clustering rationale we also develop corresponding metrics for evaluating quality of clusters found. Experiments on real datasets show that the clusters found by our algorithm successfully capture the high utility patterns in the data. Comparative experiments with other clustering algorithms further illustrate the effectiveness of our algorithm.This proceeding is published as Lakhawat, P., Mishra, M. and Somani, A. "A Novel Clustering Algorithm to Capture Utility Information in Transactional Data." In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - Volume 1: KDIR, 456-462, 2016, Porto, Portugal. DOI: 10.5220/0006092104560462. Published in SCITEPRESS Digital Library. Posted with permission.</p

Imaging of spaces of neck and mediastinum by endoscopic ultrasound

Endoscopic ultrasound (EUS) of the mediastinum was pioneered by gastroenterologists, and it was taken up by pulmonologists when the smaller-diameter endobronchial ultrasound (EBUS) scope was designed after a few years. The pulmonologists′ approach remained largely confined to entry from the trachea, but they soon realized that the esophagus was an alternative route of entry by the EBUS scope. The new generations of interventionists are facing the challenge of learning two techniques (EUS and EBUS) from two routes (esophagus and trachea). The International Association for the Study of Lung Cancer (IASLC) proposed a classification of mediastinal lymph nodes at different stations that lie within the boundaries of specific spaces. These interventionists need clear definitions of landmarks and clear techniques to identify the spaces. There are enough descriptions of spaces of the neck and the mediastinum in the literature, yet the topic mentioned above has never been discussed separately. The anatomical structures, landmarks, and boundaries of spaces will be important to interventionists in the near future during performances of endosonography. This article combines the baseline anatomy of the spaces with the actual imaging during EUS