Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and focus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy: first, we detail a deep candidate generation model and then describe a separate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.

https://dl.acm.org/doi/pdf/10.1145/2959100.2959190

Deep Neural Networks for YouTube Recommendations

Background: The accurate prediction of where faults are likely to occur in code can help direct test effort, reduce costs, and improve the quality of software. Objective: We investigate how the context of models, the independent variables used, and the modeling techniques applied influence the performance of fault prediction models. Method: We used a systematic literature review to identify 208 fault prediction studies published from January 2000 to December 2010. We synthesize the quantitative and qualitative results of 36 studies which report sufficient contextual and methodological information according to the criteria we develop and apply. Results: The models that perform well tend to be based on simple modeling techniques such as Naive Bayes or Logistic Regression. Combinations of independent variables have been used by models that perform well. Feature selection has been applied to these combinations when models are performing particularly well. Conclusion: The methodology used to build models seems to be influential to predictive performance. Although there are a set of fault prediction studies in which confidence is possible, more studies are needed that use a reliable methodology and which report their context, methodology, and performance comprehensively.

/pdf/a-systematic-literature-review-on-fault-prediction-50xbihfieh.pdf

A Systematic Literature Review on Fault Prediction Performance in Software Engineering

Microsoft has promoted ASP.NET’s new web services more than almost any other part of the.NET Framework. But despite their efforts, confusion is still widespread about what a web service is and, more importantly, what it’s meant to accomplish. This chapter introduces web services and explains their role in Microsoft’s vision of the programmable web. Along the way, you’ll learn about the open standards plumbing that allows web services to work and removes some of the confusion surrounding technical terms like WSDL (Web Service Description Language), SOAP, and UDDI (universal description, discovery, and integration).

Web Services Architecture

There are early studies to attempt users for psychiatric counseling with chatbot. They lead to changes in drinking habit based on intervention approach via chat bot. The application does not consider the user's psychiatric status through the conversations, continuous user monitoring, and ethical judgment in the intervention. We contend that more accurate and continuous emotion recognition gives better satisfaction to users who need mental health care. In addition, appropriate clinical psychological response based on ethical responses is as well. We suggest a conversational service for psychiatric counseling that is adapted methodologies to understand counseling contents based on of high-level natural language understanding (NLU), and emotion recognition based on multi-modal approach. The methodologies enable continuous observation of emotional changes sensitively. In addition, the case-based counseling response model that combines ethical judgment model provides a suitable response to clinical psychiatric counseling.

A Chatbot for Psychiatric Counseling in Mental Healthcare Service Based on Emotional Dialogue Analysis and Sentence Generation

Early study tries to use chatbot for counseling services. They changed drinking habit of who being consulted by leading them via intervene chatbot. However, the application did not concerned about psychiatric status through continuous conversation with user monitoring. Furthermore, they had no ethical judgment method that about the intervention of the chatbot. We argue that more reasonable and continuous emotion recognition will make better mental healthcare experiment. It will be more proper clinical psychiatric consolation in ethical view as well. This paper suggests a introduce a novel chatbot system for psychiatric counseling service. Our system understands content of conversation based on recent natural language processing (NLP) methods with emotion recognition. It senses emotional flow through the continuous observation of conversation. Also, we generate personalized counseling response from user input, to do this, we use additional constrains to generation model for the proper response generation which can detect conversational context, user emotion and expected reaction.

The chatbot feels you - a counseling service using emotional response generation

Weighted frequent pattern (WFP) mining is more practical than frequent pattern mining because it can consider different semantic significance (weight) of the items. For this reason, WFP mining becomes an important research issue in data mining and knowledge discovery. However, existing algorithms cannot be applied for incremental and interactive WFP mining and also for stream data mining because they are based on a static database and require multiple database scans. In this paper, we present two novel tree structures IWFPT"W"A (Incremental WFP tree based on weight ascending order) and IWFPT"F"D (Incremental WFP tree based on frequency descending order), and two new algorithms IWFP"W"A and IWFP"F"D for incremental and interactive WFP mining using a single database scan. They are effective for incremental and interactive mining to utilize the current tree structure and to use the previous mining results when a database is updated or a minimum support threshold is changed. IWFP"W"A gets advantage in candidate pattern generation by obtaining the highest weighted item in the bottom of IWFPT"W"A. IWFP"F"D ensures that any non-candidate item cannot appear before candidate items in any branch of IWFPT"F"D and thus speeds up the prefix tree and conditional tree creation time during mining operation. IWFPT"F"D also achieves the highly compact incremental tree to save memory space. To our knowledge, this is the first research work to perform single-pass incremental and interactive mining for weighted frequent patterns. Extensive performance analyses show that our tree structures and algorithms are very efficient and scalable for single-pass incremental and interactive WFP mining.

Single-pass incremental and interactive mining for weighted frequent patterns

High utility pattern (HUP) mining over data streams has become a challenging research issue in data mining. When a data stream flows through, the old information may not be interesting in the current time period. Therefore, incremental HUP mining is necessary over data streams. Even though some methods have been proposed to discover recent HUPs by using a sliding window, they suffer from the level-wise candidate generation-and-test problem. Hence, they need a large amount of execution time and memory. Moreover, their data structures are not suitable for interactive mining. To solve these problems of the existing algorithms, in this paper, we propose a novel tree structure, called HUS-tree (high utility stream tree) and a new algorithm, called HUPMS (high utility pattern mining over stream data) for incremental and interactive HUP mining over data streams with a sliding window. By capturing the important information of stream data into an HUS-tree, our HUPMS algorithm can mine all the HUPs in the current window with a pattern growth approach. Furthermore, HUS-tree is very efficient for interactive mining. Extensive performance analyses show that our algorithm is very efficient for incremental and interactive HUP mining over data streams and significantly outperforms the existing sliding window-based HUP mining algorithms.

Ho-Jin Choi

Papers

A Chatbot for Psychiatric Counseling in Mental Healthcare Service Based on Emotional Dialogue Analysis and Sentence Generation

The chatbot feels you - a counseling service using emotional response generation

Single-pass incremental and interactive mining for weighted frequent patterns

Interactive mining of high utility patterns over data streams

A framework for mining interesting high utility patterns with a strong frequency affinity