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                <text>Financial Capability and Decision Making Effects on Financial Wellbeing of Women in Community Based Organisations  </text>
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                <text>Financial Social Work (FSW) is a multi-disciplinary interactive approach that deals with financially vulnerable populations financial issues/solutions. FSW deals with individual, family and community wellbeing by increasing access to good financial services, asset-building opportunities, financial education and guidance. The two concepts newlineunderlying FSW are financial capability and financial wellbeing. Financial capability is increasingly becoming a priority for policymakers, social workers, academicians and organisations of economic and social newlinesignificance. In India, limited studies are available for measuring the financial capability of Indian citizens. While literature suggests various studies on financial capability and financial wellbeing, the focus on their combined effect has been limited. The feminisation of poverty is an newlineessential topic when discussing the capability and wellbeing of women. This refers to the increasing tendency for those in poverty to be women and children due to the reasons such as economic conditions, demographic composition and government policy. Hence, it is relevant to consider newlinewomen as the focus point of the study. The present study measures women s financial capability and examines how demographic factors influence the same. The study also explores the impact of financial capability on the decision making ability and financial wellbeing of women associated with Community Based Organisations (CBOs). It further newlineexamines whether the decision making ability mediates between financial capability and financial wellbeing. Primary data was collected from the respondents through a structured questionnaire to identify and establish these aspects. In total, 1000 women who were associated with the CBOKudumbashree in the state of Kerala, India, identified through the multistage sampling technique, participated in the survey-based study.</text>
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                <text>The dissertation entitled Analysis of Web Mining Patterns Using Custom-Built Apriori Algorithm has developed a custom-built apriori algorithm for the discovery of association rules in web log data. Web server log containing the information about all the web requests to the Christ university website is used for analysis. The methodology adapted by this research is a four step process, containing data preprocessing, frequent pattern discovery, analysis and developing a tool for implementing web mining.

The custom built apriori takes the preprocessed weblog file as input and generates the frequent folders and the relationship among them. This thesis has also developed a tool written in java for this web usage mining process. The tool assists the user to execute the custom built apriori algorithm and to view the associations among folders based on the given support and confidence values to the tool.

The web is a highly dynamic information source. Most of the organisations put information on the web because they want it to be seen by the world. Now a days the web is well beefed up with more information in an unstructured fashion. As the web and its usage continue to grow, there is an opportunity to analyse web data and extract useful knowledge from it. The objective of this research is to predict the user behaviour in interacting with the website that helps the website designer in improving the quality of website.

The dissertation is organised into 5 chapters. Chapter1, Introduction starts with a brief overview of web mining and presents the objective of the study and the problem statement. Chapter 2, Literature review, discusses background work in the field of web mining and pattern discovery. Chapter 3, methodology elaborately discusses the process used for analysing the web patterns. Chapter 4 is dedicated for results and discussion. Chapter5, conclusion, summarises the inferences concluded based on the results obtained. The chapter also discusses the limitations and challenges and concludes with future scope of the study.



KeyWords: Web Mining, Preprocessing, Web Server log, Frequent pattern</text>
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