Time series analysis and forecasting is of vital significance, owing to its widespread use in various practical domains. Time series data refers to an ordered sequence or a set of data points that a variable takes at equal time intervals. The stock market is considered to be one of the most highly complex financial systems which consist of various components or stocks, the price of which fluctuates greatly with respect to time. Stock market forecasting involves uncovering the market trends with respect to time. All the stock market investors aim to maximize the returns over their investments and minimize the risks associated. Stock markets being highly sensitive and susceptible to quick changes, the main aim of stock-trend prediction is to develop new innovative approaches to foresee the stocks that result in high profits. This research tries to analyze the time series data of the Indian stock market and build a statistical model that could efficiently predict the future stocks. INDEX TERMS ARIMA model, forecasting, stock market forecasts, time series analysis, Box-Jenkins method.

The vast amount of hidden data in huge databases has created tremendous interests in the field of data mining. This paper discusses the data analytical tools and data mining techniques to analyze the medical data as well as spatial data. Spatial data mining includes discovery of interesting and useful patterns from spatial databases by grouping the objects into clusters. This study focuses on discrete and continuous spatial medical databases on which clustering techniques are applied and the efficient clusters were formed. The clusters of arbitrary shapes are formed if the data is continuous in nature. Furthermore, this application investigated data mining techniques such as classical clustering and hierarchical clustering on the spatial data set to generate the efficient clusters. The experimental results showed that there are certain facts that are evolved and can not be superficially retrieved from raw data.

Fuzzy logic is an organized and mathematical method of handling inherently imprecise concepts through the use of membership functions, which allows membership with a certain degree. It has found application in numerous problem domains. It has been used in the interval [0, 1] fuzzy clustering, in pattern recognition and in other domains. In this paper, we introduce fuzzy logic, fuzzy clustering and an application and benefits. A case analysis has been done for various clustering algorithms in Fuzzy Clustering. It has been proved that some of the defined and available algorithms have difficulties at the borders in handling the challenges posed in collection of natural data. An analysis of two fuzzy clustering algorithms namely fuzzy c-means and Gustafson-Kessel fuzzy clustering algorithm has been analyzed.

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Purpose -To investigate changes at molecular level in lac-resole blends, occurring due to the effect of thermal stress at higher temperatures and different intervals of baking time. Design/methodology/approach -Films of lac-resole blends were applied on tin-panels and baked at 2008C for different time intervals. The baked films were examined by specular reflectance spectroscopy, as they were otherwise difficult to examine through conventional IR techniques, using KBr pellet method. The results obtained were compared and reported. Findings -When lac-resole blends are baked at 2008C, in addition to possible chromic ring structures, esters linkages are formed between lac and resole molecules through cross linkages among different reactive sites of lac and PF resin. Blend of 70 per cent lac: 30 per cent resole, baked at 2008C for 20 min was found to be the best in terms of different physico-chemical properties.Research limitations/implications -Lac-synthetic resin blends are structurally complex in nature. Chemical researches on such blends have been typically limited due to lack of modern tools. The present method, to determine molecular level changes in lac-resole blends due to heating effects, using state-of-the-art instrumentation and computational techniques, opens a new field for research and industry. Practical implications -Lac and its blends retain their significance in the surface coatings and food applications, in the formulation of lacquers, varnishes and in the finishing industry. This study could have significant implication for such industries from application point of view. Originality/value -As of now, there is no report of specular reflectance data on lac-synthetic resin blends. This paper represents the first attempt to obtain and correlate reflectance data on such blends. It also highlights the convenience of the method and the scope of sophisticated data analysis, including derivative spectrometry.

PurposeTo determine molecular level changes occurring in lac resin, due to the effect of thermal stress at different levels of temperature and baking times.Design/methodology/approachFilms of lac resin were applied on tin panels and baked at 100 and 200°C for different time intervals. The baked films were examined by specular reflectance spectroscopy, as they were otherwise difficult to examine through conventional IR techniques, using KBr pellet method. The results obtained were compared and correlated with work reported by earlier authors using wet chemical methods.FindingsNo significant spectroscopic change was observed on heating lac resin films at 100°C for different time intervals, as compared to air‐dried (parent) lac films. However, it was observed that when the films were baked at 200°C, the spectroscopic data indicated anhydride formation in the oligomers, due to heating effects.Research limitations/implicationsChemical researches on lac resin have been typically limited by lack of modern tools, due to the difficult and unique nature of the material. The present method to determine molecular level changes in lac due to heating effects, using state‐of‐art instrumentation and computational technique opens a new vista in this field of research.Practical implicationsLac resin still has a significant place in the surface coating industry, typically in food applications, insulating vanishes, etc. The results obtained indicate that lac‐based baking compositions, when baked at 200°C, exhibit improved characteristics, in terms of adhesion, scratch hardness and even acid resistance. Such an improvement can be attributed unequivocally to the formation of anhydride linkage as evident from spectroscopic data.Originality/valueAs of now, there is no report of specular reflectance data on lac resin and its derivatives. This paper represents the first attempt to obtain and correlate reflectance data on lac. It also highlights the convenience of the method and the scope of sophisticated data analysis, using computational methods.

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Nowadays data growth is directly proportional to time and it is a major challenge to store the data in an organised fashion. Document clustering is the solution for organising relevant documents together. In this paper, a web clustering algorithm namely WDC-KABC is proposed to cluster the web documents effectively. The proposed algorithm uses the features of both K-means and Artificial Bee Colony (ABC) clustering algorithm. In this paper, ABC algorithm is employed as the global search optimizer and K-means is used for refining the solutions. Thus, the quality of the cluster is improved. The performance of WDC-KABC is analysed with four different datasets (webkb, wap, rec0 and 7sectors). The proposed algorithm is compared with existing algorithms such as K-means, Particle Swarm Optimization, Hybrid of Particle Swarm Optimization and K-means and Ant Colony Optimization. The experimental results of WDC-KABC are satisfactory, in terms of precision, recall, f-measure, accuracy and error rate.