Classification of spatial data can be difficult with existing methods due to the large numbers an... more Classification of spatial data can be difficult with existing methods due to the large numbers and sizes of spatial data sets. The task becomes even more difficult when we consider continuous spatial data streams. Data streams require a classifier that can be built and rebuilt repeatedly in near real time. This paper presents an approach to deal with this challenge. Our approach uses the Peano Count Tree (P-tree), which provides a lossless, compressed and data-mining-ready representation (data structure) for spatial data. This data structure can be applied in many classification techniques. In this paper we focus on Bayesian classification. A Bayesian classifier is a statistical classifier, which uses the Bayes theorem to predict class membership as a conditional probability that a given data sample falls into a particular class. In this paper we demonstrate how P-trees can improve the classification of spatial data when using a Bayesian classifier. We also introduce the use of information gain calculations with Bayesian classification to improve its accuracy. The use of a P-tree based Bayesian classifier can not only make classification more effective on spatial data, but also can reduce the build time of the classifier considerably. This improvement in build time makes it feasible for use with streaming data.
Peano count trees (P-trees) provide efficient, lossless, data mining ready representations of tab... more Peano count trees (P-trees) provide efficient, lossless, data mining ready representations of tabular data and make possible the mining of multiple very large data sets, including time-sequences of Remotely Sensed Imagery (RSI) and micro-array gene expression datasets (MA). Each MA dataset presents a one-time, gene expression level map of thousands of genes subjected to hundreds of conditions. MA data has traditionally been archived as text abstracts (e.g., Medline abstracts). An important multimedia application is to integrate macro-scale analysis of RSI with the micro-scale analysis of MA across multiple plant organisms. This is truly a multimedia data mining problem. Most multimedia data is mined by extracting pertinent features into tables, then mining the tables. P-trees are a convenient technology to mine all such multimedia data.
There is an increase in the demand for data mining applications on the web. With the increase in ... more There is an increase in the demand for data mining applications on the web. With the increase in the size of data sets there is also a demand for scalable generic solutions. Scalability and generic data mining models can be provided with the use of distributed computing. In this paper we propose the use of a multilayered framework for a distributed data mining system. A multi-layered architecture can take advantage of the latest technological advances in hardware to provide efficient solutions and also allow the easy addition of new data mining and data capture components to the basic system. A multi-layered architecture also facilitates an iterative development process. In this paper we show the use of a high end data mining engine with generic data mining models on the server side and a client that can capture the client requirements over the web.
Clustering is automated identification of groups of objects based on similarity. In clustering tw... more Clustering is automated identification of groups of objects based on similarity. In clustering two major research issues are scalability and the requirement of domain knowledge to determine input parameters. Most approaches suggest the use of sampling to address the issue of scalability. However, sampling does not guarantee the best solution and can cause significant loss in accuracy. Most approaches also require the use of domain knowledge, trial and error techniques, or exhaustive searching to figure out the required input parameters. In this paper we introduce a new clustering technique based on the set square distance. Cluster membership is determined based on the set squared distance to the respective cluster. As in the case of mean for k-means and median for k-medoids, the cluster is represented by the entire cluster of points for each evaluation of membership. The set square distance for all n items can be computed efficiently in O(n) using a vertical data structure and a few pre-computed values. Special ordering of the set square distance is used to break the data into the "natural" clusters compared to the need of a known k for k-means or kmedoids type of partition clustering. Superior results are observed when the new clustering technique is compared with the classical k-means clustering. To prove the cluster quality and the resolution of the unknown k, data sets with known classes such as the iris data, the uci_kdd network intrusion data, and synthetic data are used. The scalability of the proposed technique is proved using a large RSI data set.
Data arising from genomic and proteomic experiments is amassing at high speeds resulting in huge ... more Data arising from genomic and proteomic experiments is amassing at high speeds resulting in huge amounts of raw data; consequently, the need for analyzing such biological data -the understanding of which is still lagging way behind -has been prominently solicited in the post-genomic era we are currently witnessing. In this paper we attempt to analyze annotated genome data by applying a very central data-mining technique known as association rule mining with the aim of discovering rules capable of yielding deeper insights into this type of data. We propose a new technique capable of using domain knowledge in the form of queries in order to efficiently mine only the subset of the associations that are of interest to researcher in an incremental and interactive mode.
Genomics data has many properties that make it different from "typical" relational data. The pres... more Genomics data has many properties that make it different from "typical" relational data. The presence of multi-valued attributes as well as the large number of null values led us to a P-tree-based bit-vector representation in which matching 1-values were counted to evaluate similarity between genes. Quantitative information such as the number of interactions was also included in the classifier. Interaction information allowed us to extend the known properties of one protein with information on its interacting neighbors. Different feature attributes were weighted independently. Relevance of different attributes was systematically evaluated through optimization of weights using a genetic algorithm. The AROC value for the classified list was used as the fitness function for the genetic algorithm.
New data mining tools and algorithms are available for vertical data mining communities for scala... more New data mining tools and algorithms are available for vertical data mining communities for scalable and efficient data mining to discover the hidden nuggets from huge repositories of data. Most of the traditional data mining algorithms do not scale on these huge datasets. This is due to insufficient computational resources, currently available on a single machine for running these applications. Distributed computing based in a vertical data structure presents an effective solution for dealing with the computational load incurred in data mining applications. However, currently there exists no such API for a vertical data structure on a distributed environment. As a result an API for a distributed vertical data mining is necessary for steering the future development of data mining algorithms to provide further scalability for large datasets. In this paper we present an API for a distributed vertical data mining environment providing different levels of transparency and flexibility using MPI as a distributed computing environment and C++ as a programming language.
Accuracy is one of the major issues for a classifier. Currently there exist a range of classifier... more Accuracy is one of the major issues for a classifier. Currently there exist a range of classifiers with different degrees of accuracy directly related to computational complexity. In this paper we are presenting an approach to improve the classification accuracy of an existing P-Tree based Bayesian classification technique. The new approach has increased the granularity between two conditional probability calculations by using a bit-based approach rather than the existing band-based approach. This approach enables the complete elimination of the naïve assumption. The new approach maintains the same computational cost as the previous method. This approach outperforms the existing P-Tree based Bayesian classifier, a Bayesian belief network and a Euclidian distance based KNN classifier, in terms of accuracy for a particular set of spatial data collected for precision agriculture.
The explosion of genomic data made possible by advances in parallel, high-throughput technologies... more The explosion of genomic data made possible by advances in parallel, high-throughput technologies in the area of molecular biology, has ushered in a new era in the area of Bioinformatics. During the last many years, efforts concentrated on sequencing the genome of organisms. Current emphasis lies in extracting meaningful information from this huge DNA sequence and expression data. The techniques
In this paper, we introduce the vertical set square distance (VSSD) technique that is designed to... more In this paper, we introduce the vertical set square distance (VSSD) technique that is designed to efficiently and scalably measure the total variation of a set about a fixed point in large datasets. The set can be any projected subspace of any vector space, including oblique subspaces (not just dimensional subspaces). VSSD can determine the closeness of a point to a set of points in a dataset, which can be very useful for classification, clustering and outlier detection tasks. The technique employs a vertical data structure called the Predicate-tree (P-tree) 1 . Performance evaluations based on both synthetic and real-world datasets show that VSSD technology is fast, accurate and scales well to very large datasets, as compared to similar techniques utilizing horizontal record-based data structure.
Classification of spatial data can be difficult with existing methods due to the large numbers an... more Classification of spatial data can be difficult with existing methods due to the large numbers and sizes of spatial data sets. The task becomes even more difficult when we consider continuous spatial data streams. Data streams require a classifier that can be built and rebuilt repeatedly in near real time. This paper presents an approach to deal with this challenge. Our approach uses the Peano Count Tree (P-tree), which provides a lossless, compressed and data-mining-ready representation (data structure) for spatial data. This data structure can be applied in many classification techniques. In this paper we focus on Bayesian classification. A Bayesian classifier is a statistical classifier, which uses the Bayes theorem to predict class membership as a conditional probability that a given data sample falls into a particular class. In this paper we demonstrate how P-trees can improve the classification of spatial data when using a Bayesian classifier. We also introduce the use of information gain calculations with Bayesian classification to improve its accuracy. The use of a P-tree based Bayesian classifier can not only make classification more effective on spatial data, but also can reduce the build time of the classifier considerably. This improvement in build time makes it feasible for use with streaming data.
Peano count trees (P-trees) provide efficient, lossless, data mining ready representations of tab... more Peano count trees (P-trees) provide efficient, lossless, data mining ready representations of tabular data and make possible the mining of multiple very large data sets, including time-sequences of Remotely Sensed Imagery (RSI) and micro-array gene expression datasets (MA). Each MA dataset presents a one-time, gene expression level map of thousands of genes subjected to hundreds of conditions. MA data has traditionally been archived as text abstracts (e.g., Medline abstracts). An important multimedia application is to integrate macro-scale analysis of RSI with the micro-scale analysis of MA across multiple plant organisms. This is truly a multimedia data mining problem. Most multimedia data is mined by extracting pertinent features into tables, then mining the tables. P-trees are a convenient technology to mine all such multimedia data.
There is an increase in the demand for data mining applications on the web. With the increase in ... more There is an increase in the demand for data mining applications on the web. With the increase in the size of data sets there is also a demand for scalable generic solutions. Scalability and generic data mining models can be provided with the use of distributed computing. In this paper we propose the use of a multilayered framework for a distributed data mining system. A multi-layered architecture can take advantage of the latest technological advances in hardware to provide efficient solutions and also allow the easy addition of new data mining and data capture components to the basic system. A multi-layered architecture also facilitates an iterative development process. In this paper we show the use of a high end data mining engine with generic data mining models on the server side and a client that can capture the client requirements over the web.
Clustering is automated identification of groups of objects based on similarity. In clustering tw... more Clustering is automated identification of groups of objects based on similarity. In clustering two major research issues are scalability and the requirement of domain knowledge to determine input parameters. Most approaches suggest the use of sampling to address the issue of scalability. However, sampling does not guarantee the best solution and can cause significant loss in accuracy. Most approaches also require the use of domain knowledge, trial and error techniques, or exhaustive searching to figure out the required input parameters. In this paper we introduce a new clustering technique based on the set square distance. Cluster membership is determined based on the set squared distance to the respective cluster. As in the case of mean for k-means and median for k-medoids, the cluster is represented by the entire cluster of points for each evaluation of membership. The set square distance for all n items can be computed efficiently in O(n) using a vertical data structure and a few pre-computed values. Special ordering of the set square distance is used to break the data into the "natural" clusters compared to the need of a known k for k-means or kmedoids type of partition clustering. Superior results are observed when the new clustering technique is compared with the classical k-means clustering. To prove the cluster quality and the resolution of the unknown k, data sets with known classes such as the iris data, the uci_kdd network intrusion data, and synthetic data are used. The scalability of the proposed technique is proved using a large RSI data set.
Data arising from genomic and proteomic experiments is amassing at high speeds resulting in huge ... more Data arising from genomic and proteomic experiments is amassing at high speeds resulting in huge amounts of raw data; consequently, the need for analyzing such biological data -the understanding of which is still lagging way behind -has been prominently solicited in the post-genomic era we are currently witnessing. In this paper we attempt to analyze annotated genome data by applying a very central data-mining technique known as association rule mining with the aim of discovering rules capable of yielding deeper insights into this type of data. We propose a new technique capable of using domain knowledge in the form of queries in order to efficiently mine only the subset of the associations that are of interest to researcher in an incremental and interactive mode.
Genomics data has many properties that make it different from "typical" relational data. The pres... more Genomics data has many properties that make it different from "typical" relational data. The presence of multi-valued attributes as well as the large number of null values led us to a P-tree-based bit-vector representation in which matching 1-values were counted to evaluate similarity between genes. Quantitative information such as the number of interactions was also included in the classifier. Interaction information allowed us to extend the known properties of one protein with information on its interacting neighbors. Different feature attributes were weighted independently. Relevance of different attributes was systematically evaluated through optimization of weights using a genetic algorithm. The AROC value for the classified list was used as the fitness function for the genetic algorithm.
New data mining tools and algorithms are available for vertical data mining communities for scala... more New data mining tools and algorithms are available for vertical data mining communities for scalable and efficient data mining to discover the hidden nuggets from huge repositories of data. Most of the traditional data mining algorithms do not scale on these huge datasets. This is due to insufficient computational resources, currently available on a single machine for running these applications. Distributed computing based in a vertical data structure presents an effective solution for dealing with the computational load incurred in data mining applications. However, currently there exists no such API for a vertical data structure on a distributed environment. As a result an API for a distributed vertical data mining is necessary for steering the future development of data mining algorithms to provide further scalability for large datasets. In this paper we present an API for a distributed vertical data mining environment providing different levels of transparency and flexibility using MPI as a distributed computing environment and C++ as a programming language.
Accuracy is one of the major issues for a classifier. Currently there exist a range of classifier... more Accuracy is one of the major issues for a classifier. Currently there exist a range of classifiers with different degrees of accuracy directly related to computational complexity. In this paper we are presenting an approach to improve the classification accuracy of an existing P-Tree based Bayesian classification technique. The new approach has increased the granularity between two conditional probability calculations by using a bit-based approach rather than the existing band-based approach. This approach enables the complete elimination of the naïve assumption. The new approach maintains the same computational cost as the previous method. This approach outperforms the existing P-Tree based Bayesian classifier, a Bayesian belief network and a Euclidian distance based KNN classifier, in terms of accuracy for a particular set of spatial data collected for precision agriculture.
The explosion of genomic data made possible by advances in parallel, high-throughput technologies... more The explosion of genomic data made possible by advances in parallel, high-throughput technologies in the area of molecular biology, has ushered in a new era in the area of Bioinformatics. During the last many years, efforts concentrated on sequencing the genome of organisms. Current emphasis lies in extracting meaningful information from this huge DNA sequence and expression data. The techniques
In this paper, we introduce the vertical set square distance (VSSD) technique that is designed to... more In this paper, we introduce the vertical set square distance (VSSD) technique that is designed to efficiently and scalably measure the total variation of a set about a fixed point in large datasets. The set can be any projected subspace of any vector space, including oblique subspaces (not just dimensional subspaces). VSSD can determine the closeness of a point to a set of points in a dataset, which can be very useful for classification, clustering and outlier detection tasks. The technique employs a vertical data structure called the Predicate-tree (P-tree) 1 . Performance evaluations based on both synthetic and real-world datasets show that VSSD technology is fast, accurate and scales well to very large datasets, as compared to similar techniques utilizing horizontal record-based data structure.
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Papers by Amal Perera