Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3

Weka tool Related Abstracts

3 Improved FP-Growth Algorithm with Multiple Minimum Supports Using Maximum Constraints

Authors: Dina M. Ibrahim, Elsayeda M. Elgaml, Elsayed A. Sallam

Abstract:

Association rule mining is one of the most important fields of data mining and knowledge discovery. In this paper, we propose an efficient multiple support frequent pattern growth algorithm which we called “MSFP-growth” that enhancing the FP-growth algorithm by making infrequent child node pruning step with multiple minimum support using maximum constrains. The algorithm is implemented, and it is compared with other common algorithms: Apriori-multiple minimum supports using maximum constraints and FP-growth. The experimental results show that the rule mining from the proposed algorithm are interesting and our algorithm achieved better performance than other algorithms without scarifying the accuracy.

Keywords: Association Rules, FP-growth, multiple minimum supports, Weka tool

Procedia PDF Downloads 300
2 A Proposed Approach for Emotion Lexicon Enrichment

Authors: Amira M. Idrees, Amr Mansour Mohsen, Hesham Ahmed Hassan

Abstract:

Document Analysis is an important research field that aims to gather the information by analyzing the data in documents. As one of the important targets for many fields is to understand what people actually want, sentimental analysis field has been one of the vital fields that are tightly related to the document analysis. This research focuses on analyzing text documents to classify each document according to its opinion. The aim of this research is to detect the emotions from text documents based on enriching the lexicon with adapting their content based on semantic patterns extraction. The proposed approach has been presented, and different experiments are applied by different perspectives to reveal the positive impact of the proposed approach on the classification results.

Keywords: Sentimental analysis, Document Analysis, Emotion Detection, Weka tool, NRC lexicon

Procedia PDF Downloads 289
1 Documents Emotions Classification Model Based on TF-IDF Weighting Measure

Authors: Amira M. Idrees, Amr Mansour Mohsen, Hesham Ahmed Hassan

Abstract:

Emotions classification of text documents is applied to reveal if the document expresses a determined emotion from its writer. As different supervised methods are previously used for emotion documents’ classification, in this research we present a novel model that supports the classification algorithms for more accurate results by the support of TF-IDF measure. Different experiments have been applied to reveal the applicability of the proposed model, the model succeeds in raising the accuracy percentage according to the determined metrics (precision, recall, and f-measure) based on applying the refinement of the lexicon, integration of lexicons using different perspectives, and applying the TF-IDF weighting measure over the classifying features. The proposed model has also been compared with other research to prove its competence in raising the results’ accuracy.

Keywords: Classification Algorithms, Emotion Detection, Weka tool, TF-IDF

Procedia PDF Downloads 225