A genetic algorithm coupled with tree-based pruning for mining closed association rules

Due to the voluminous amount of itemsets that are generated, the association rules extracted from these itemsets contain redundancy, and designing an effective approach to address this issue is of paramount importance. Although multiple algorithms were proposed in recent years for mining closed association rules most of them underperform in terms of run time or memory. Another issue that remains challenging is the nature of the dataset. While some of the existing algorithms perform well on dense datasets others perform well on sparse datasets. This paper aims to handle these drawbacks by using a genetic algorithm for mining closed association rules. Recent studies have shown that genetic algorithms perform better than conventional algorithms due to their bitwise operations of crossover and mutation. Bitwise operations are predominantly faster than conventional approaches and bits consume lesser memory thereby improving the overall performance of the algorithm. To address the redundancy in the mined association rules a tree-based pruning algorithm has been designed here. This works on the principle of minimal antecedent and maximal consequent. Experiments have shown that the proposed approach works well on both dense and sparse datasets while surpassing existing techniques with regard to run time and memory

Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem

Hashtag is an iconic feature to retrieve the hot topics of discussion on Twitter or other social networks. This paper incorporates the pattern mining approaches to improve the accuracy of retrieving the relevant information and speeding up the search performance. A novel algorithm called PM-HR (Pattern Mining for Hashtag Retrieval) is designed to first transform the set of tweets into a transactional database by considering two different strategies (trivial and temporal). After that, the set of the relevant patterns is discovered, and then used as a knowledge-based system for finding the relevant tweets based on users\u27 queries under the similarity search process. Extensive results are carried out on large and different tweet collections, and the proposed PM-HR outperforms the baseline hashtag retrieval approaches in terms of runtime, and it is very competitive in terms of accuracy

Recurrent neural network with density-based clustering for group pattern detection in energy systems

This research explores a new direction in power system technology and develops a new framework for pattern group discovery from large power system data. The efficient combination between the recurrent neural network and the density-based clustering enables to find the group patterns in the power system. The power system data is first collected in multiple time series data and trained by the recurrent neural network to find simple patterns. The simple patterns are then studied, and analyzed with the density-based clustering algorithm to identify the group of patterns. The solution was analyzed in two case studies (pattern discovery and outlier detection) specifically for power systems. The results show the advantages of the proposed framework and a clear superiority compared to state-of-the-art approaches, where the average correlation in group pattern detection is 90% and in group outlier detection more than 80% of both true-positive and true-negative rates.publishedVersio

Pattern mining under different conditions

New requirements and demands on pattern mining arise in modern applications, which cannot be fulfilled using conventional methods. For example, in scientific research, scientists are more interested in unknown knowledge, which usually hides in significant but not frequent patterns. However, existing itemset mining algorithms are designed for very frequent patterns. Furthermore, scientists need to repeat an experiment many times to ensure reproducibility. A series of datasets are generated at once, waiting for clustering, which can contain an unknown number of clusters with various densities and shapes. Using existing clustering algorithms is time-consuming because parameter tuning is necessary for each dataset. Many scientific datasets are extremely noisy. They contain considerably more noises than in-cluster data points. Most existing clustering algorithms can only handle noises up to a moderate level. Temporal pattern mining is also important in scientific research. Existing temporal pattern mining algorithms only consider pointbased events. However, most activities in the real-world are interval-based with a starting and an ending timestamp. This thesis developed novel pattern mining algorithms for various data mining tasks under different conditions. The first part of this thesis investigates the problem of mining less frequent itemsets in transactional datasets. In contrast to existing frequent itemset mining algorithms, this part focus on itemsets that occurred not that frequent. Algorithms NIIMiner, RaCloMiner, and LSCMiner are proposed to identify such kind of itemsets efficiently. NIIMiner utilizes the negative itemset tree to extract all patterns that occurred less than a given support threshold in a top-down depth-first manner. RaCloMiner combines existing bottom-up frequent itemset mining algorithms with a top-down itemset mining algorithm to achieve a better performance in mining less frequent patterns. LSCMiner investigates the problem of mining less frequent closed patterns. The second part of this thesis studied the problem of interval-based temporal pattern mining in the stream environment. Interval-based temporal patterns are sequential patterns in which each event is aligned with a starting and ending temporal information. The ability to handle interval-based events and stream data is lacking in existing approaches. A novel intervalbased temporal pattern mining algorithm for stream data is described in this part. The last part of this thesis studies new problems in clustering on numeric datasets. The first problem tackled in this part is shape alternation adaptivity in clustering. In applications such as scientific data analysis, scientists need to deal with a series of datasets generated from one experiment. Cluster sizes and shapes are different in those datasets. A kNN density-based clustering algorithm, kadaClus, is proposed to provide the shape alternation adaptability so that users do not need to tune parameters for each dataset. The second problem studied in this part is clustering in an extremely noisy dataset. Many real-world datasets contain considerably more noises than in-cluster data points. A novel clustering algorithm, kenClus, is proposed to identify clusters in arbitrary shapes from extremely noisy datasets. Both clustering algorithms are kNN-based, which only require one parameter k. In each part, the efficiency and effectiveness of the presented techniques are thoroughly analyzed. Intensive experiments on synthetic and real-world datasets are conducted to show the benefits of the proposed algorithms over conventional approaches

Machine Learning Approaches for Breast Cancer Survivability Prediction

Breast cancer is one of the leading causes of cancer death in women. If not diagnosed early, the 5-year survival rate of patients is just about 26\%. Furthermore, patients with similar phenotypes can respond differently to the same therapies, which means the therapies might not work well for some of them. Identifying biomarkers that can help predict a cancer class with high accuracy is at the heart of breast cancer studies because they are targets of the treatments and drug development. Genomics data have been shown to carry useful information for breast cancer diagnosis and prognosis, as well as uncovering the disease’s mechanism. Machine learning methods are powerful tools to find such information. Feature selection methods are often utilized in supervised learning and unsupervised learning tasks to deal with data containing a large number of features in which only a small portion of them are useful to the classification task. On the other hand, analyzing only one type of data, without reference to the existing knowledge about the disease and the therapies, might mislead the findings. Effective data integration approaches are necessary to uncover this complex disease. In this thesis, we apply and develop machine learning methods to identify meaningful biomarkers for breast cancer survivability prediction after a certain treatment. They include applying feature selection methods on gene-expression data to derived gene-signatures, where the initial genes are collected concerning the mechanism of some drugs used breast cancer therapies. We also propose a new feature selection method, named PAFS, and apply it to discover accurate biomarkers. In addition, it has been increasingly supported that, sub-network biomarkers are more robust and accurate than gene biomarkers. We proposed two network-based approaches to identify sub-network biomarkers for breast cancer survivability prediction after a treatment. They integrate gene-expression data with protein-protein interactions during the optimal sub-network searching process and use cancer-related genes and pathways to prioritize the extracted sub-networks. The sub-network search space is usually huge and many proteins interact with thousands of other proteins. Thus, we apply some heuristics to avoid generating and evaluating redundant sub-networks

Artificial intelligence of medical things for disease detection using ensemble deep learning and attention mechanism

In this paper, we present a novel paradigm for disease detection. We build an artificial intelligence based system where various biomedical data are retrieved from distributed and homogeneous sensors. We use different deep learning architectures (VGG16, RESNET, and DenseNet) with ensemble learning and attention mechanisms to study the interactions between different biomedical data to detect and diagnose diseases. We conduct extensive testing on biomedical data. The results show the benefits of using deep learning technologies in the field of artificial intelligence of medical things to diagnose diseases in the healthcare decision-making process. For example, the disease detection rate using the proposed methodology achieves 92%, which is greatly improved compared to the higher-level disease detection models.publishedVersio