Electricity consumption prediction in smart homes and its effective management are global concerns. One of the most important inventions to assist human living, electricity is used by residential users as well as commercial operations. These users of

This issue of the IDA journal, is the second issue for our 23 rd year of publication.It contains 12 articles representing a wide range of topics related to the theoretical and applied research in the field of Intelligent Data Analysis.The first two articles are about text and sentiment analysis in IDA.Liu et al. in the first article of this issue argue that since volume of short text data increases rapidly it is essential to organize and summarize these data automatically where topic model is one of the effective approaches.The authors introduce a model which makes use of Recurrent Neural Networks to learn relationships.By using the learned relationships, the authors introduce Bigrams, which can display topics of interest.Through experiments on two open-source and real-world datasets, the authors demonstrate better coherence in topic discovery.Along the same line of research, Gravoc et al. in the second article of this issue argue that Sentiment Polarity Detection (SPD) (classifying texts by "positive" or "negative" orientation) has become more important and challenging task in recent years.The authors explore different n-gram based text representation models in order to determine the most valuable model for the representation of text documents in various languages, which can be used successfully by ML classification techniques for solving SPD tasks.Their proposed n-gram models were used in conjunction with k-Nearest Neighbourhood (kNN), Support Vector Machine (SVM) and Maximum Entropy (MaxEnt) algorithms to determine opinion polarity of the proposed movie reviews.Significant improvements over the baselines have been demonstrated in this article.The second group of articles in this issue are about computational methods in IDA.Johnson and Giraud-Carrier in the third article of this issue argue that based on empirical evidence, ensembles with adequate levels of pairwise diversity among a set of accurate member algorithms can significantly outperform any of the individual algorithms.The authors show that there is natural tension between the pairwise diversity of ensemble members and their individual accuracy.While efficient ensembles can be built with stronger forms of diversity, they also suffer in overall accuracy.On the other hand, ensembles built with weaker forms of diversity can be very accurate, but tend to be significantly more computationally expensive.Along the same line of research, Zhou et al. in the next article argue that ensembles with adequate levels of pairwise diversity among a set of accurate member algorithms can significantly outperform any of the individual algorithms.The authors show that there is a natural tension between the pairwise diversity of ensemble members and their individual accuracy.The authors also demonstrate that while efficient ensembles can be built with stronger forms of diversity, they also suffer on overall accuracy.Yoo and Bow in the next article of this group discuss that spatial co-location mining is a useful tool for discovering spatial association patterns of feature sets which are frequently observed together in nearby geographic space.The authors introduce the problem of mining reduced sets of colocation patterns in order to concisely represent interesting spatial relationship patterns.They propose an algorithmic framework to discover maximal co-location patterns and closed co-location patterns as well as all prevalent co-location patterns, and present the algorithm details for each pattern discovery.

Due to the rapid growth of web platforms such as blogs, discussion forums, peer-to-peer networks, and various other types of social media, Sentiment Polarity Detection (SPD) (classifying texts by “positive” or “negative” orientation) has become more

Association Rule Mining (ARM) is a fundamental data mining task that is time-consuming on big datasets. Thus, developing new scalable algorithms for this problem is desirable. Recently, Bee Swarm Optimization (BSO)-based meta-heuristics were shown effective to reduce the time required for ARM. But these approaches were applied only on small or medium scale databases. To perform ARM on big databases, a promising approach is to design parallel algorithms using the massively parallel threads of a GPU processor. While some GPU-based ARM algorithms have been developed, they only benefit from GPU parallelism during the evaluation step of solutions obtained by the BSO-metaheuristics. This paper improves this approach by parallelizing the other steps of the BSO process (diversification and intensification). Based on these novel ideas, three novel algorithms are presented, i) DRGPU (Determination of Regions on GPU), ii) SAGPU (Search Area on GPU, and, iii) ALLGPU (All steps on GPU). These solutions are analyzed and empirically compared on benchmark datasets. Experimental results show that ALLGPU outperforms the three other approaches in terms of speed up. Moreover, results confirm that ALLGPU outperforms the state-of-the-art GPU-based ARM approaches on big ARM databases such as the Webdocs dataset. Furthermore, ALLGPU is extended to mine big frequent graphs and results demonstrate its superiority over the state-of-the-art D-Mine algorithm for frequent graph mining on the large Pokec social network dataset.

Data clustering is one of the most important tasks in machine learning and data mining, which aims to discover natural structure of the data, identify relationships between observations inside data sets, or detect outliers. Clustering is traditionall

Many researches have exploited textual data, such as news, online blogs, and financial reports, in order to predict stock price movements effectively. Previous studies formed the task as a classification problem predicting upward or downward movement of stock prices from text documents. Such an app roach, however, may be deemed inappropriate when combined with sentiment analysis. In financial documents, same words may convey different sentiments across different sectors; if documents from multiple sectors are learned simultaneously, performance can deteriorate. Therefore, we conducted sentiment analysis of 8-K financial reports of firms sector by sector. In particular, we also employed distributed representation for predicting stock price movements. Experiment results show that our approach improves prediction performance by 25.4% over the baseline model, and that the direction of post-announcement stock price movements shifts accordingly with the polarity of the sentiment of reports. Not only does our model improve predictability, but also provides visualizations, which may assist agents actively trading in the field with understanding the drivers for the observed stock movements. The two main aspects of our model, predictability and interpretability, will provide meaningful insights to help decision-makers in the industry with time-split trading decisions or data-driven detection of promising companies.

Data is shared among different organizations for mutual benefit. Data mining techniques are utilized to discover valuable knowledge for decision-making. However, data mining poses a threat to disclose the sensitive information. Thus, the sensitive kn

Visualization is a useful technique in data analysis, especially, in the initial stage, data exploration. Since high-dimensional data is not visible, dimensionality reduction techniques are usually used to reduce the data to a lower dimension, say tw

This paper presents the design and performance evaluation of a host-based misuse intrusion detection system for the Linux operating system. The proposed system employs a feature extraction technique based on principal component analysis, which is called Eigentraces, of operating system call trace d ata, and k-nearest neighbor algorithm for classification. The design is evaluated on the ADFA-LD dataset which entails one normal and six attack classes. Feature vectors are formed from fixed-size system call trace raw data through windowing and the principal component analysis, and serve as templates for the training phase. Classification of system call trace data that is in the form of feature vectors which are formulated through the Eigentraces procedure is accomplished using the k-nearest-neighbor algorithm. Two variants of the misuse intrusion detection system designs were evaluated through a simulation study on the ADFA-LD dataset: one design considered only two classes, namely normal and attack classes while the second design considered seven classes, namely one normal and six attack classes. In both cases the proposed design demonstrated very high performance. In overall, the misuse intrusion detection system was able to detect the attacks and predict the type of the attacks.