Computer Sciences Commons^™

High Utility Itemsets Identification In Big Data, Ashish Tamrakar

UNLV Theses, Dissertations, Professional Papers, and Capstones

High utility itemset mining is an important data mining problem which considers profit factors besides quantity from the transactional database. It helps find the most valuable products/items that are difficult to track using only the frequent data mining set. An item that has a high-profit value might be rare in the transactional database despite its tremendous importance. While there are many existing algorithms which generate comparatively large candidate sets while finding high utility itemsets, the major focus is to reduce the computational time significantly with the introduction of pruning strategies. Another aspect of high utility itemset mining is to compute …

Enhancing The Draft Assembly With Minhash, Saju Varghese

UNLV Theses, Dissertations, Professional Papers, and Capstones

In this thesis, we report on the use of minhash techniques to improve the draft assembly of a genome mapping. More specifically, we use minhash to compare the scaffolds of sea urchin and sea cucumber genomes.

One of the main contributions of this thesis is the implementation of minhash with the Message Passing Interface (MPI) utilizing Intel Phi co-processors. It is shown that our implementation significantly reduces the processing time for identification of k-mer similarities.

Partitioning Of Minimotifs Based On Function With Improved Prediction Accuracy, Sanguthevar Rajasekaran, Tian Mi, Jerlin Camilus Merlin, Aaron Oommen, Patrick R. Gradie, Martin R. Schiller

Life Sciences Faculty Research

Background

Minimotifs are short contiguous peptide sequences in proteins that are known to have a function in at least one other protein. One of the principal limitations in minimotif prediction is that false positives limit the usefulness of this approach. As a step toward resolving this problem we have built, implemented, and tested a new data-driven algorithm that reduces false-positive predictions.

Methodology/Principal Findings

Certain domains and minimotifs are known to be strongly associated with a known cellular process or molecular function. Therefore, we hypothesized that by restricting minimotif predictions to those where the minimotif containing protein and target protein have …

Effects Of Similarity Metrics On Document Clustering, Rushikesh Veni

UNLV Theses, Dissertations, Professional Papers, and Capstones

Document clustering or unsupervised document classification is an automated process of grouping documents with similar content. A typical technique uses a similarity function to compare documents. In the literature, many similarity functions such as dot product or cosine measures are proposed for the comparison operator.

For the thesis, we evaluate the effects a similarity function may have on clustering. We start by representing a document and a query, both as a vector of high-dimensional space corresponding to the keywords followed by using an appropriate distance measure in k-means to compute similarity between the document vector and the query vector to …