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Priyanka Tiwari

Bio: Priyanka Tiwari is an academic researcher. The author has contributed to research in topics: K-optimal pattern discovery & Perspective (graphical). The author has an hindex of 1, co-authored 1 publications receiving 24 citations.

Papers
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01 Jan 2012
TL;DR: It will be seen how sequential pattern mining is not applicable for mining item set from multidimensional data and why multiddimensional pattern Mining is necessary.
Abstract: Data mining is the task of discovering interesting patterns from large amounts of data. There are many data mining tasks, such as classification, clustering, association rule mining, and sequential pattern mining. Sequential pattern mining is the process of finding the relationships between occurrences of sequential events, to find if there exists any specific order of the occurrences. It is a data mining task which finds the set of frequent items in sequence database. It is applicable in a wide range of applications since many types of data sets are in a time related format. Besides mining sequential patterns in a single dimension, mining multidimensional sequential patterns can give us more informative and useful patterns. Due to the huge increase in data volume and also quite large search space, efficient solutions for finding patterns in multidimensional sequence data are nowadays very important. In this paper, we discuss about sequential pattern mining, sequential pattern, methods used in sequential pattern mining and we will see how sequential pattern mining is not applicable for mining item set from multidimensional data. And why multidimensional pattern mining is necessary.

25 citations

Journal ArticleDOI
TL;DR: It is suggested that music therapy has positive outcomes as a treatment approach in children with autism with one study showing no significant relationship.
Abstract: Music therapy is an alternative form of therapy that has positive impact in many areas of physical and mental health. The purpose of this study was to review researches systematically on the impact of music therapy in children with autism spectrum disorder. PRISMA model was followed including 17 researches out of 27 researches published in various journals related to music therapy from sources like PubMed and Scopus over 6 years and were analyzed in detail. Findings suggested that music therapy has positive outcomes as a treatment approach in children with autism with one study showing no significant relationship. Most of the studies were done on social skills and communication, stereotype behavior and motor coordination and less on other domains like social affect and responsiveness, understanding others gestures and perspective, resistance to change and echolalia. Future studies need to focus on the domains less studied on.

Cited by
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01 Jan 2002

9,314 citations

Journal ArticleDOI
TL;DR: This work focuses on the analysis of “complex” sequential data by means of interesting sequential patterns and shows how pattern structures along with projections are able to enumerate more meaningful patterns and increase the computing efficiency of the approach.
Abstract: Nowadays data-sets are available in very complex and heterogeneous ways. Mining of such data collections is essential to support many real-world applications ranging from healthcare to marketing. In this work, we focus on the analysis of “complex” sequential data by means of interesting sequential patterns. We approach the problem using the elegant mathematical framework of formal concept analysis and its extension based on “pattern structures”. Pattern structures are used for mining complex data (such as sequences or graphs) and are based on a subsumption operation, which in our case is defined with respect to the partial order on sequences. We show how pattern structures along with projections (i.e. a data reduction of sequential structures) are able to enumerate more meaningful patterns and increase the computing efficiency of the approach. Finally, we show the applicability of the presented method for discovering and analysing interesting patient patterns from a French healthcare data-set on cancer. T...

40 citations

Journal ArticleDOI
TL;DR: A multiparadigm version of CanadarmTutor is explained, which allows providing a richer set of tutoring services than what could be offered with previous single paradigm versions of CanadArmTutor.
Abstract: To assist learners during problem-solving activities, an intelligent tutoring system (ITS) has to be equipped with domain knowledge that can support appropriate tutoring services. Providing domain knowledge is usually done by adopting one of the following paradigms: building a cognitive model, specifying constraints, integrating an expert system, and using data mining algorithms to learn domain knowledge. However, for some ill-defined domains, each single paradigm may present some advantages and limitations in terms of the required resources for deploying it, and tutoring support that can be offered. To address this issue, we propose using a multiparadigm approach. In this paper, we explain how we have applied this idea in CanadarmTutor, an ITS for learning to operate the Canadarm2 robotic arm. To support tutoring services in this ill-defined domain, we have developed a multiparadigm model combining: 1) a cognitive model to cover well-defined parts of the task and spatial reasoning, 2) a data mining approach for automatically building a task model from user solutions for ill-defined parts of the task, and 3) a 3D path-planner to cover other parts of the task for which no user data are available. The multiparadigm version of CanadarmTutor allows providing a richer set of tutoring services than what could be offered with previous single paradigm versions of CanadarmTutor.

22 citations

Book ChapterDOI
01 Jan 2013
TL;DR: This paper formally defines context-dependent sequential patterns and highlights relevant properties that lead to an efficient extraction algorithm and conducts experimental evaluation on real-world data and demonstrates performance issues.
Abstract: Traditional sequential patterns do not take into account contextual information associated with sequential data. For instance, when studying purchases of customers in a shop, a sequential pattern could be “frequently, customers buy products A and B at the same time, and then buy product C”. Such a pattern does not consider the age, the gender or the socio-professional category of customers. However, by taking into account contextual information, a decision expert can adapt his/her strategy according to the type of customers. In this paper, we focus on the analysis of a given context (e.g., a category of customers) by extracting context-dependent sequential patterns within this context. For instance, given the context corresponding to young customers, we propose to mine patterns of the form “buying products A and B then product C is a general behavior in this population” or “buying products B and D is frequent for young customers only”. We formally define such context-dependent sequential patterns and highlight relevant properties that lead to an efficient extraction algorithm. We conduct our experimental evaluation on real-world data and demonstrate performance issues.

19 citations

Posted Content
TL;DR: This is the first study that incorporates the time-dependent sequence-order, quantitative information, utility factor, and auxiliary dimension in multi-dimensional sequences and proposes a novel framework named MDUS to extract Multi-Dimensional Utility-oriented Sequential useful patterns.
Abstract: Knowledge extraction from database is the fundamental task in database and data mining community, which has been applied to a wide range of real-world applications and situations. Different from the support-based mining models, the utility-oriented mining framework integrates the utility theory to provide more informative and useful patterns. Time-dependent sequence data is commonly seen in real life. Sequence data has been widely utilized in many applications, such as analyzing sequential user behavior on the Web, influence maximization, route planning, and targeted marketing. Unfortunately, all the existing algorithms lose sight of the fact that the processed data not only contain rich features (e.g., occur quantity, risk, profit, etc.), but also may be associated with multi-dimensional auxiliary information, e.g., transaction sequence can be associated with purchaser profile information. In this paper, we first formulate the problem of utility mining across multi-dimensional sequences, and propose a novel framework named MDUS to extract Multi-Dimensional Utility-oriented Sequential useful patterns. Two algorithms respectively named MDUS_EM and MDUS_SD are presented to address the formulated problem. The former algorithm is based on database transformation, and the later one performs pattern joins and a searching method to identify desired patterns across multi-dimensional sequences. Extensive experiments are carried on five real-life datasets and one synthetic dataset to show that the proposed algorithms can effectively and efficiently discover the useful knowledge from multi-dimensional sequential databases. Moreover, the MDUS framework can provide better insight, and it is more adaptable to real-life situations than the current existing models.

15 citations